A method, device, electronic device and storage medium for inverting biomass carbon storage

Abstract

The present invention discloses a biomass carbon stock inversion method, device, electronic device and storage medium. By applying a two-level generative adversarial network, the present invention can effectively improve the accuracy and details of the generated graph, and can further solve the problem that a single generative adversarial network is difficult to generate detailed data; by combining sparse representation, generative adversarial network and unmanned aerial vehicle hyperspectral imaging technology, the efficiency and accuracy of biomass and carbon stock inversion are significantly improved, the dimensionality reduction processing of hyperspectral data is carried out through sparse representation technology, and a sparse basis dictionary that can effectively extract key spectral information is constructed. In combination with a sparse coding algorithm, the redundancy and computational complexity of the data are greatly reduced, the speed of data processing is improved, and the important feature information in the hyperspectral data is retained, so that the subsequent inversion process is more efficient and accurate. The invention can be widely used in the field of data processing technology.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a biomass carbon stock inversion method, device, electronic equipment and storage medium. Background Art

[0002] With the increasing severity of environmental protection and climate change issues, the global demand for monitoring and assessment of biomass and carbon stocks is increasing. As important indicators for measuring the health of forest and grassland ecosystems, biomass and carbon stocks are of great significance to the study of carbon cycle and climate change. At present, remote sensing monitoring using drones equipped with hyperspectral sensors has become an effective means of obtaining biomass and carbon storage data over large areas. However, traditional hyperspectral data processing methods often face some technical difficulties and challenges when applied to biomass and carbon storage inversion.

[0003] First, existing hyperspectral data processing methods usually need to process a large amount of high-dimensional data. The data has high dimensions and a lot of redundant information, resulting in high computational complexity and low data processing efficiency. In this case, even if high-precision biomass and carbon storage data can be obtained, it is often accompanied by extremely high computational costs, affecting the real-time and practicality of the inversion results. In addition, since traditional methods cannot effectively extract and utilize key feature information in hyperspectral data, it is easy to cause insufficient accuracy of the inversion results. Especially when dealing with complex terrain and diverse vegetation types, the adaptability and robustness of the inversion model are poor, and it is impossible to stably provide accurate biomass and carbon storage estimation results under different environmental conditions.

[0004] When used for biomass and carbon storage inversion, traditional generative adversarial networks can generate distribution maps that are close to real data. However, due to the complex training process of their generators and discriminators, they are prone to overfitting or mode collapse, resulting in the generated biomass and carbon storage distribution maps lacking details and being unable to accurately reflect the actual biomass and carbon storage distribution characteristics.

[0005] Therefore, existing technologies have obvious deficiencies in hyperspectral data processing, biomass and carbon stock inversion, and the application of generative adversarial networks, and cannot meet the requirements of modern ecological monitoring and carbon stock assessment for efficiency, accuracy, and robustness. The defects of existing technologies limit the application of UAV hyperspectral technology in large-scale ecosystem monitoring. Summary of the Invention

[0006] The present invention aims to at least partially address the limitations of related technologies. To this end, the present invention provides a biomass carbon stock inversion method, apparatus, electronic device, and storage medium that can efficiently and accurately perform biomass carbon stock inversion.

[0007] In one aspect, an embodiment of the present invention provides a method for inverting biomass carbon storage, comprising:

[0008] Acquire hyperspectral image data of a sample plot area in the target area; the hyperspectral image data includes multi-band reflectance spectral information of surface vegetation in the sample plot area; the sample plot area includes multiple sample plots of different terrain types in the target area;

[0009] Preprocessing of hyperspectral image data; preprocessing includes radiation correction, geometric correction and noise removal;

[0010] A sparse basis dictionary is constructed based on the preprocessed hyperspectral image data, and a sparse coding algorithm is used to extract the feature vector.

[0011] Based on the feature vector, a first distribution map of biomass and carbon storage is generated using a preset first generative adversarial network;

[0012] Based on the first distribution map, a second distribution map of biomass and carbon storage is generated using a preset second generative adversarial network;

[0013] Among them, the first generative adversarial network and the second generative adversarial network are constructed based on the adversarial training of the generator and the discriminator;

[0014] An inversion model is constructed based on the mapping relationship between the second distribution map and the characteristic vector;

[0015] The inverse model was applied to the entire area of the target region to obtain the third distribution map of biomass and carbon storage in the target region.

[0016] Optionally, obtaining hyperspectral image data of a sample plot area in a target area includes the following steps:

[0017] In response to a control command from a target object, a target UAV is controlled to fly to a sample plot area of a target area based on preset operating parameters; a hyperspectral imaging sensor is provided on the target UAV;

[0018] Obtain multi-band reflectance spectral information collected by the hyperspectral imaging sensor of the target UAV during its flight over the sample area;

[0019] According to the flight path and time series of the target UAV, all multi-band reflectance spectral information is spliced and registered to obtain the hyperspectral image data of the sample area.

[0020] Optionally, a sparse basis dictionary is constructed based on the preprocessed hyperspectral image data, and a sparse coding algorithm is used to extract a feature vector, including the following steps:

[0021] The initial sparse basis dictionary is constructed based on the preprocessed hyperspectral image data; the expression of the initial sparse basis dictionary is: ,in, represents the i-th basis vector, k is the number of basis vectors in the initial sparse basis dictionary;

[0022] The initial sparse basis dictionary is optimized to obtain an optimized sparse basis dictionary. The expression of the optimized sparse basis dictionary is:

[0023] ;

[0024] in, Represents an optimized sparse basis dictionary; represents the preprocessed hyperspectral image data, Represents the surface coordinates, Indicates wavelength; Represents the sparse coefficient corresponding to the i-th basis vector; is the regularization parameter;

[0025] The sparse coding algorithm is used to perform sparse representation on the preprocessed hyperspectral image data to obtain the sparse representation coefficient vector of the spectral vector corresponding to each hyperspectral pixel. The expression of the sparse representation coefficient vector is:

[0026] ;

[0027] in, represents the sparse representation coefficient vector; Represents the spectral vector corresponding to the hyperspectral pixel; represents the initial sparse basis dictionary; Represents the sparse coefficient corresponding to the hyperspectral pixel; and is the regularization parameter; represents the sparse coefficient corresponding to the jth pixel around the hyperspectral pixel, and m is the number of pixels around the hyperspectral pixel;

[0028] The initial feature vector is obtained by extracting the sparse representation coefficient vector in combination with the optimized sparse basis dictionary;

[0029] The initial feature vector is sparsely coded and dimensionally reduced to obtain the feature vector.

[0030] Optionally, the method further comprises the following steps:

[0031] Obtain actual distribution maps of biomass and carbon storage in the sample area;

[0032] A first generator is constructed based on a first mapping function between the feature vector and the actual distribution graph;

[0033] A first discriminant is constructed based on a first discriminant function that distinguishes the generated result of the first generator from the actual distribution graph;

[0034] Generate a first training distribution graph through a first generator according to the feature vector;

[0035] Generate a first discrimination result by a first discriminator according to the first training distribution map and the actual distribution map;

[0036] Based on the first discrimination result, performing first adversarial training on the first generator and the first discriminator according to a preset first loss function;

[0037] Optimize the parameters of the first generator and the first discriminator according to the results of the first adversarial training; return to the step of generating a first training distribution map through the first generator according to the feature vector until the first number of iterations is reached to obtain a first generative adversarial network.

[0038] Optionally, the method further comprises the following steps:

[0039] Obtain actual distribution maps of biomass and carbon storage in the sample area;

[0040] A second generator is constructed based on a second mapping function between the first distribution map and the actual distribution map;

[0041] A second discriminant is constructed based on a second discriminant function that distinguishes the generated result of the second generator from the actual distribution graph;

[0042] Generating a second training distribution map by a second generator according to the first distribution map;

[0043] Generating a second discrimination result by a second discriminator according to the second training distribution map and the actual distribution map;

[0044] Based on the second discrimination result, performing second adversarial training on the second generator and the second discriminator according to a preset second loss function;

[0045] Parameters of the second generator and the second discriminator are optimized according to the results of the second adversarial training; and the step of generating a second training distribution map through the second generator according to the first distribution map is returned to execute until the second number of iterations is reached to obtain a second generative adversarial network.

[0046] Optionally, constructing an inversion model based on the mapping relationship between the second distribution map and the eigenvector comprises the following steps:

[0047] Obtain actual distribution maps of biomass and carbon storage in the sample area;

[0048] According to the mapping function between the eigenvector and the actual distribution map, the mapping relationship between the second distribution map and the eigenvector is optimized by least square method, and the inversion model is obtained by fitting.

[0049] Optionally, the method further comprises the following steps:

[0050] Obtain a realistic distribution map of biomass and carbon storage in the target area;

[0051] Based on the third distribution map, a classification analysis of the terrain and vegetation types of the target area is performed to obtain a terrain feature map and a vegetation type map of the target area;

[0052] According to the terrain feature map and vegetation type map, statistical values under different terrain conditions and vegetation types are obtained; the statistical values include biomass statistical values and carbon storage statistical values;

[0053] Based on the terrain characteristic map and vegetation type map, combined with the actual distribution map, the statistical values under different terrain conditions and vegetation types in the target area are evaluated and analyzed to obtain an analytical report on the biomass and carbon storage of the target area.

[0054] In another aspect, an embodiment of the present invention provides a biomass carbon stock inversion device, comprising:

[0055] The first module is used to obtain hyperspectral image data of a sample plot area in the target area; the hyperspectral image data includes multi-band reflectance spectral information of surface vegetation in the sample plot area; the sample plot area includes multiple sample plots of different terrain types in the target area;

[0056] The second module is used to preprocess the hyperspectral image data; the preprocessing includes radiation correction, geometric correction and noise removal;

[0057] The third module is used to construct a sparse basis dictionary based on the preprocessed hyperspectral image data and extract the feature vector using the sparse coding algorithm;

[0058] A fourth module is configured to generate a first distribution map of biomass and carbon storage based on the feature vector using a preset first generative adversarial network;

[0059] A fifth module is configured to generate a second distribution map of biomass and carbon storage based on the first distribution map using a preset second generative adversarial network;

[0060] Among them, the first generative adversarial network and the second generative adversarial network are constructed based on the adversarial training of the generator and the discriminator;

[0061] A sixth module is used to construct an inversion model based on the mapping relationship between the second distribution map and the characteristic vector;

[0062] The seventh module is used to apply the inversion model to the entire area of the target region to obtain a third distribution map of biomass and carbon storage in the target region.

[0063] Optionally, the device further comprises:

[0064] The eighth module is used to obtain the actual distribution map of biomass and carbon storage in the sample area;

[0065] A ninth module is configured to construct a first generator based on a first mapping function between the feature vector and the actual distribution graph;

[0066] A tenth module is configured to construct a first discriminator based on a first discriminant function for distinguishing a generated result of the first generator from an actual distribution graph;

[0067] An eleventh module is configured to generate a first training distribution graph through a first generator according to the feature vector;

[0068] A twelfth module is configured to generate a first discrimination result through a first discriminator according to the first training distribution map and the actual distribution map;

[0069] A thirteenth module is configured to perform a first adversarial training on the first generator and the first discriminator according to a preset first loss function based on the first discrimination result;

[0070] The fourteenth module is used to optimize the parameters of the first generator and the first discriminator according to the results of the first adversarial training; return to execute the eleventh module until the first number of iterations is reached to obtain the first generative adversarial network.

[0071] Optionally, the device further comprises:

[0072] The fifteenth module is used to obtain the actual distribution map of biomass and carbon storage in the sample area;

[0073] A sixteenth module is configured to construct a second generator based on a second mapping function between the first distribution map and the actual distribution map;

[0074] A seventeenth module is used to construct a second discriminator based on a second discriminant function that distinguishes the generated result of the second generator from the actual distribution graph;

[0075] An eighteenth module is configured to generate a second training distribution map through a second generator according to the first distribution map;

[0076] A nineteenth module is configured to generate a second discrimination result through a second discriminator according to the second training distribution map and the actual distribution map;

[0077] A twentieth module is configured to perform second adversarial training on the second generator and the second discriminator according to a preset second loss function based on the second discrimination result;

[0078] The twenty-first module is used to optimize the parameters of the second generator and the second discriminator according to the results of the second adversarial training; and return to execute the eighteenth module until the second number of iterations is reached to obtain the second generative adversarial network.

[0079] On the other hand, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned biomass carbon stock inversion method.

[0080] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned biomass carbon stock inversion method.

[0081] The embodiment of the present invention obtains hyperspectral image data of a sample area in a target area; the hyperspectral image data includes multi-band reflectance spectral information of surface vegetation in the sample area; the sample area includes multiple sample areas of different terrain types in the target area; the hyperspectral image data is preprocessed; the preprocessing includes radiation correction, geometric correction and noise removal; a sparse basis dictionary is constructed based on the preprocessed hyperspectral image data, and a sparse coding algorithm is used to extract a feature vector; based on the feature vector, a first distribution map of biomass and carbon storage is generated using a preset first generative adversarial network; based on the first distribution map, a second distribution map of biomass and carbon storage is generated using a preset second generative adversarial network; wherein the first generative adversarial network and the second generative adversarial network are constructed based on adversarial training of a generator and a discriminator; an inversion model is constructed based on the mapping relationship between the second distribution map and the feature vector; the inversion model is applied to the entire area of the target area to obtain a third distribution map of biomass and carbon storage in the target area. By applying a two-level generative adversarial network, the present invention can effectively improve the accuracy and details of the generated image, and can further solve the problem that a single generative adversarial network is difficult to generate detailed data. In addition, the present invention uses sparse representation technology to effectively reduce the redundancy of hyperspectral data, extract key features, thereby reducing computational costs, and provide stable and accurate inversion results even under complex environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0083] Figure 1 A schematic diagram of an implementation environment for biomass carbon stock inversion provided by an embodiment of the present invention;

[0084] Figure 2 A schematic flow chart of a biomass carbon stock inversion method provided in an embodiment of the present invention;

[0085] Figure 3 A schematic diagram of the expanded flow of step S100 provided in an embodiment of the present invention;

[0086] Figure 4 A schematic diagram of the expanded flow of step S300 provided in an embodiment of the present invention;

[0087] Figure 5 A schematic diagram of the architecture principle of a primary generative adversarial network provided by an embodiment of the present invention;

[0088] Figure 6 A schematic diagram of the architecture principle of an advanced generative adversarial network provided by an embodiment of the present invention;

[0089] Figure 7 A schematic diagram of the expanded flow of step S600 provided in an embodiment of the present invention;

[0090] Figure 8 A schematic diagram of the overall process principle of the biomass carbon stock inversion method provided by an embodiment of the present invention;

[0091] Figure 9 A schematic structural diagram of a biomass carbon stock inversion device provided in an embodiment of the present invention;

[0092] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0094] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0095] References to "embodiments" in this disclosure mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the disclosure. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0096] It is understandable that the biomass carbon stock inversion method provided in the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0097] To facilitate understanding of the technical solutions of the present invention, the following are the technical terms that may appear in the embodiments of the present invention:

[0098] Sparse representation: Sparse representation is a technical method for signal processing and data analysis, which simplifies data by representing signals or data as a combination of a set of sparse, linearly independent basis vectors.

[0099] In short, sparse representation describes complex datasets using only a few important features (rather than all information), thereby achieving dimensionality reduction and efficient computation. It is widely used in fields such as image processing and pattern recognition. However, it has been less widely used in biomass carbon stock inversion.

[0100] Hyperspectral region: Hyperspectral region refers to multi-band spectral data acquired using hyperspectral imaging technology. Each band records the reflection or absorption information of an object at a different wavelength. Unlike traditional color images (red, green, and blue), hyperspectral imaging can capture data from dozens or even hundreds of bands, thus providing more detailed spectral characteristics of materials. Hyperspectral data can accurately identify and distinguish different material components, such as vegetation, soil, or water, and is a key technology for biomass and carbon storage analysis.

[0101] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the invention. Figure 1 , the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to the network in a wireless or wired manner to complete data transmission and exchange.

[0102] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0103] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0104] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0105] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a biomass carbon stock inversion method. The following is explained using the example of the biomass carbon stock inversion method being applied in the server 101. It can be understood that the biomass carbon stock inversion method can also be applied in the terminal 102.

[0106] Reference Figure 2 , Figure 2 This is a flowchart of a biomass carbon stock inversion method applied to a server according to an embodiment of the present invention. The execution subject of the biomass carbon stock inversion method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0107] S100, obtaining hyperspectral image data of a sample area in a target area;

[0108] The hyperspectral image data includes multi-band reflectance spectral information of surface vegetation in the sample plot area; the sample plot area includes multiple sample plots of different terrain types in the target area;

[0109] It should be noted that, in some embodiments, Figure 3As shown, step S100 may include the following steps: S101, in response to the control instruction of the target object, controlling the target UAV to fly to the sample area of the target area based on preset operating parameters; a hyperspectral imaging sensor is provided on the target UAV; S102, obtaining multi-band reflectance spectrum information collected by the hyperspectral imaging sensor during the flight of the target UAV in the sample area; S103, according to the flight path and time series of the target UAV, all multi-band reflectance spectrum information is spliced and aligned to obtain hyperspectral image data of the sample area.

[0110] For example, in some specific implementations, the embodiments of the present invention first utilize a drone equipped with a hyperspectral imaging sensor to fly over a target area to obtain hyperspectral image data covering the target area, wherein the hyperspectral image data contains multi-band reflectance spectral information of surface vegetation; wherein, multiple sample plots can be set up on different terrain types in the target area, for example, 50 sample plots, each with an area of approximately 1 hectare, as collection points for training samples and verification samples; the drone is equipped with a hyperspectral imaging sensor, and according to a preset flight path, it performs multiple flight missions under different altitudes and terrain conditions, and collects hyperspectral image data covering the entire study area.

[0111] Optionally, in some preferred implementations, step S100 may be implemented as follows:

[0112] S11. Select a UAV flight path and altitude suitable for the target area, and determine the UAV flight speed v and flight altitude h, where the flight speed v should be adjusted according to the frame rate f and ground resolution r of the hyperspectral imaging sensor;

[0113] S12. Calculate the width W and length L of the ground surface that can be covered in a single flight based on the drone's flight altitude h and the sensor's field of view FOV:

[0114] ;

[0115] ;

[0116] in, and are the field of view angles of the hyperspectral imaging sensor in the horizontal and vertical directions, respectively;

[0117] S13. During the flight of the UAV, the multi-band reflectance spectrum data (multi-band reflectance spectrum information) collected by the hyperspectral imaging sensor is obtained in real time, and the spectral reflectance of each image pixel is recorded. ,in Indicated by wavelength The reflectivity at wavelengths ranging from visible light to near infrared , so that hyperspectral data can reflect the spectral characteristics of surface vegetation;

[0118] S14. Based on the flight path and time sequence of the UAV, the continuously acquired hyperspectral image data are spliced and registered to generate complete hyperspectral image data covering the target area. , where x and y are the surface coordinates, is the wavelength;

[0119] S15. In the acquired hyperspectral image data, calibrate the area and boundary of the surface vegetation and extract the effective area data containing multi-band reflectance spectrum information. ,in, Represents spectral data with biomass and carbon storage inversion significance in the target area.

[0120] S200, preprocessing the hyperspectral image data;

[0121] Among them, preprocessing includes radiation correction, geometric correction and noise removal; it can be achieved by using corresponding processing algorithms or data processing software. The specific processing method is not the focus of the process logic of the present invention and will not be repeated here.

[0122] S300, constructing a sparse basis dictionary based on the preprocessed hyperspectral image data, and extracting a feature vector using a sparse coding algorithm;

[0123] It should be noted that, in some embodiments, Figure 4 As shown, step S300 may include the following steps: S301, constructing an initial sparse basis dictionary based on the preprocessed hyperspectral image data; the expression of the initial sparse basis dictionary is: ,in, represents the i-th basis vector, k is the number of basis vectors in the initial sparse basis dictionary;

[0124] S302, optimizing the initial sparse basis dictionary to obtain an optimized sparse basis dictionary; the expression of the optimized sparse basis dictionary is:

[0125] ;

[0126] in, Represents an optimized sparse basis dictionary; represents the preprocessed hyperspectral image data, Represents the surface coordinates, Indicates wavelength; Represents the sparse coefficient corresponding to the i-th basis vector; is the regularization parameter;

[0127] S303, using a sparse coding algorithm to perform sparse representation on the pre-processed hyperspectral image data, and obtain a sparse representation coefficient vector corresponding to the spectral vector of each hyperspectral pixel; the expression of the sparse representation coefficient vector is:

[0128] ;

[0129] in, represents the sparse representation coefficient vector; Represents the spectral vector corresponding to the hyperspectral pixel; represents the initial sparse basis dictionary; Represents the sparse coefficient corresponding to the hyperspectral pixel; and is the regularization parameter; represents the sparse coefficient corresponding to the jth pixel around the hyperspectral pixel, and m is the number of pixels around the hyperspectral pixel;

[0130] S304, extracting an initial feature vector based on the sparse representation coefficient vector and the optimized sparse basis dictionary;

[0131] S305 , performing sparse coding and dimensionality reduction processing on the initial feature vector to obtain a feature vector.

[0132] For example, in some specific implementations, a sparse basis dictionary may be constructed and a sparse coding algorithm may be used to extract the most representative feature vectors in the hyperspectral image data for dimensionality reduction while retaining key spectral information in the hyperspectral image data.

[0133] Optionally, in some preferred implementations, step S300 may be implemented as follows:

[0134] S31. Hyperspectral image data based on target area , build a sparse basis dictionary ,in, represents the i-th basis vector, k is the number of basis vectors in the dictionary, The build is optimized based on:

[0135] ;

[0136] in, is the optimized sparse basis dictionary, is the corresponding sparse coefficient, is a regularization parameter used to control the norm of the dictionary vector to ensure that the constructed dictionary can effectively represent the key spectral information in the hyperspectral data and reduce noise interference;

[0137] S32, using sparse coding algorithm Perform sparse representation, and for each hyperspectral pixel spectral vector X, obtain each spectral vector The sparse representation coefficient vector :

[0138] ;

[0139] in, and is the regularization parameter, is the sparse coefficient of the surrounding pixels, which is used to consider spatial consistency. This allows the generated sparse coefficient to not only reflect the local spectral characteristics but also consider the spectral consistency between regions, which is beneficial to the accuracy of the hyperspectral data collected by UAVs in the inversion of carbon storage in local areas.

[0140] S33, according to the sparse representation coefficient vector Extracting hyperspectral image data The most representative eigenvector in :

[0141] ;

[0142] in, is the smoothing parameter, x represents the spatial coordinates of the hyperspectral image data, that is, the position of the pixel in the image, that is, the horizontal coordinate, and y is the vertical coordinate of this point. Furthermore, x' means the position of the adjacent pixels, that is, the adjacent coordinates used when smoothing, and y' is the same. D* means the optimized sparse basis dictionary, is a weight function used to measure the similarity between adjacent pixels to ensure that when extracting the feature vector, not only the local key spectral information is retained, but also the data of different regions can be smoothly transitioned, thereby improving the representativeness and stability of the feature vector in the inversion process;

[0143] S34, yes All hyperspectral pixels in the image are sparsely coded and dimensionally reduced to generate sparse representation hyperspectral image data of the target area. ,in is the dimension-reduced feature vector of the j-th pixel, and m is the number of pixels in the hyperspectral image.

[0144] S400: Generate a first distribution map of biomass and carbon storage based on the feature vector using a preset first generative adversarial network;

[0145] In some optional embodiments, the method may further include the following steps: obtaining an actual distribution map of biomass and carbon storage in the sample plot area; constructing a first generator based on a first mapping function between the feature vector and the actual distribution map; constructing a first discriminant based on a first discriminant function that distinguishes the generation result of the first generator from the actual distribution map; generating a first training distribution map through the first generator according to the feature vector; generating a first discrimination result through the first discriminator according to the first training distribution map and the actual distribution map; based on the first discrimination result, performing a first adversarial training on the first generator and the first discriminator according to a preset first loss function; optimizing the parameters of the first generator and the first discriminator according to the result of the first adversarial training; returning to execute the step of generating the first training distribution map through the first generator according to the feature vector until the first number of iterations is reached to obtain a first generative adversarial network.

[0146] For example, in some specific embodiments, a primary generative adversarial network (i.e., a first generative adversarial network) can be constructed, wherein a primary generator of the primary generative adversarial network is used to generate a biomass and carbon storage distribution map corresponding to the hyperspectral image data after dimensionality reduction, and a primary discriminator is used to distinguish the generated biomass and carbon storage distribution map from the actual biomass and carbon storage data. The primary generative adversarial network is trained through an adversarial process between the primary generator and the primary discriminator.

[0147] Optionally, in some preferred embodiments, as Figure 5 As shown in the figure, constructing a primary generative adversarial network can be achieved as follows:

[0148] S41. Based on the hyperspectral image data Y after dimensionality reduction, a primary generative adversarial network is constructed. The primary generative adversarial network includes a primary generator and the primary discriminator ;

[0149] S42, Primary Generator Receive the reduced feature vector As input, generate corresponding biomass and carbon storage distribution maps , the goal of the primary generator is to learn the mapping function : , so that the generated biomass and carbon stock distribution maps are close to the real data;

[0150] S43, primary discriminator The input is the generated biomass and carbon stock distribution map and real biomass and carbon storage data The goal of the primary discriminator output is to distinguish the generated biomass and carbon storage distribution maps from the real data, that is, to learn a discriminant function : , where 1 represents real data and 0 represents generated data;

[0151] S44, through the primary generator and the primary discriminator The adversarial training process between them optimizes the parameters of the primary generator and the discriminator. The loss function of the primary generator is defined as:

[0152] ;

[0153] in, and Represents the real biomass and carbon storage data respectively And the eigenvector after dimensionality reduction The expectation on the primary discriminator The goal is to maximize the accuracy of discriminating real data, and the primary generator The goal is to minimize the ability of the primary discriminator to distinguish the generated data;

[0154] S45. Optimize the primary generator of the primary generative adversarial network through multiple rounds of iterative training and the primary discriminator The parameters of the generated biomass and carbon storage distribution maps are Visually and statistically close to real data .

[0155] S500: Based on the first distribution map, generate a second distribution map of biomass and carbon storage using a preset second generative adversarial network;

[0156] Among them, the first generative adversarial network and the second generative adversarial network are constructed based on the adversarial training of the generator and the discriminator;

[0157] In some optional embodiments, the method may further include the following steps: obtaining an actual distribution map of biomass and carbon storage in the sample plot area; constructing a second generator based on a second mapping function between the first distribution map and the actual distribution map; constructing a second discriminant based on a second discriminant function that distinguishes the generation result of the second generator from the actual distribution map; generating a second training distribution map through the second generator according to the first distribution map; generating a second discrimination result through the second discriminator according to the second training distribution map and the actual distribution map; based on the second discrimination result, performing a second adversarial training on the second generator and the second discriminator according to a preset second loss function; optimizing the parameters of the second generator and the second discriminator according to the result of the second adversarial training; returning to execute the step of generating a second training distribution map through the second generator according to the first distribution map, until the second number of iterations is reached, and obtaining a second generative adversarial network.

[0158] For example, in some specific implementations, embodiments of the present invention further construct an advanced generative adversarial network (i.e., a second generative adversarial network). The advanced generator of the advanced generative adversarial network further optimizes the generated results based on the biomass and carbon storage distribution maps generated by the primary generative adversarial network, making the biomass and carbon storage distribution maps more detailed and accurate. The advanced discriminator distinguishes the results generated by the primary generative adversarial network from real data. The training of the advanced generative adversarial network is carried out through an adversarial process between the advanced generator and the advanced discriminator, so that the biomass and carbon storage distribution maps generated by the advanced generator are more consistent with the distribution characteristics of real data.

[0159] Optionally, in some preferred embodiments, as Figure 6 As shown in Figure 2, building an advanced generative adversarial network can be achieved as follows:

[0160] S51. Biomass and carbon storage distribution maps generated based on primary generative adversarial networks , building an advanced generative adversarial network, which includes an advanced generator and high-level discriminators ;

[0161] S52, Advanced Generator Receive primary generated biomass and carbon stock distribution map As input, generate optimized biomass and carbon stock distribution maps , the goal of the advanced generator is to learn more complex mapping functions , optimize the biomass and carbon stock distribution maps at the detailed level, making them more refined and accurate;

[0162] S53, Advanced Discriminator The input is the biomass and carbon stock distribution map generated by the advanced generator and real biomass and carbon storage data , the discriminator output goal is to distinguish the biomass and carbon storage distribution maps generated by advanced methods from the real data, that is, to learn the discriminant function , where 1 represents real data and 0 represents generated data;

[0163] S54, through the advanced generator and high-level discriminators The adversarial training process between them optimizes the parameters of the high-level generator and the high-level discriminator. The loss function of the high-level generator is defined as:

[0164] ;

[0165] in, and Represents the real biomass and carbon storage data respectively and distribution maps of primary biomass and carbon storage Expectation on high-level discriminators The goal is to maximize the discrimination of real data, the advanced generator The goal is to minimize the ability of the high-level discriminator to distinguish high-level generated data;

[0166] S55. Optimize the advanced generator of the advanced generative adversarial network through multiple rounds of iterative training and high-level discriminators Parameters that make the advanced generator generate biomass and carbon stock distribution maps It is more consistent with the distribution characteristics of real data at the detailed level.

[0167] S600, constructing an inversion model based on the mapping relationship between the second distribution map and the characteristic vector;

[0168] It should be noted that, in some embodiments, Figure 7 As shown, step S600 may include the following steps: S601, obtaining the actual distribution map of biomass and carbon storage in the sample area; S602, according to the mapping function between the eigenvector and the actual distribution map, using the least squares method to optimize the error of the mapping relationship between the second distribution map and the eigenvector, and fitting to obtain the inversion model.

[0169] For example, in some specific embodiments, an inversion model can be constructed based on the biomass and carbon storage distribution map (i.e., the second distribution map) generated by the trained advanced generative adversarial network. The inversion model uses the mapping relationship between the generated carbon storage biomass and carbon storage distribution map and the hyperspectral feature vector of the target area to estimate the biomass and carbon storage of the target area.

[0170] Optionally, in some preferred implementations, constructing the inversion model may be specifically implemented as follows:

[0171] S61. Biomass and carbon storage distribution maps generated by advanced generative adversarial networks , build an inversion model , the inversion model is used to generate the distribution map and the hyperspectral feature vector of the target area Establish a mapping relationship between them and estimate the biomass and carbon storage in the target area;

[0172] S62, Inversion Model The construction process includes defining the mapping function ,in, Represents the real biomass and carbon storage data of the target area by fitting the training samples;

[0173] S63. Biomass and carbon storage distribution maps generated using the least squares method Real data from the target area The error between them is defined as:

[0174] ;

[0175] Where N is the number of training samples, represents the Euclidean norm, represents the sparsity regularization term of the model parameters, is a regularization parameter used to control the complexity of the model and prevent overfitting.

[0176] S700: Apply the inversion model to the entire target area to obtain a third distribution map of biomass and carbon storage in the target area.

[0177] For example, in some specific implementations, the inversion model can be applied to the hyperspectral image data of the target area to estimate the biomass and carbon stock distribution in the target area.

[0178] Optionally, in some preferred embodiments, estimating the biomass and carbon stock distribution within the target area can be achieved as follows:

[0179] S71, the optimized inversion model Hyperspectral image data applied to the target area , according to the hyperspectral feature vector of each pixel , calculate biomass and carbon stock estimates for pixels :

[0180] ;

[0181] in, is a weighting function based on pixel position and wavelength, which is used to adjust the contribution of different spectral bands to biomass and carbon storage estimation. is the hyperspectral feature vector and sparse basis dictionary The similarity kernel function is used to capture the nonlinear relationship between local spectral features and carbon storage. is the sparse coding coefficient, which indicates the contribution of different basis vectors to a specific pixel. is the dictionary basis vector, and are the minimum and maximum wavelength ranges of the hyperspectral data, respectively;

[0182] S72, traverse all pixels in the target area, through the inversion model Generate complete biomass and carbon stock distribution maps :

[0183] ;

[0184] in, It is the weight matrix between pixel position and pixel number, which is used to integrate the estimation results of multiple pixels. is a smoothing function of adjacent pixels, used to perform spatial smoothing on biomass and carbon storage. is the standard deviation of the Gaussian kernel function, which controls the degree of smoothness, and Represents the coordinate range of the target area in the x and y directions respectively.

[0185] In some optional embodiments, the method may further include the following steps: obtaining a true distribution map of biomass and carbon storage in the target area; based on the third distribution map, performing a classification analysis of the terrain and vegetation types of the target area to obtain a terrain characteristic map and a vegetation type map of the target area; according to the terrain characteristic map and the vegetation type map, obtaining statistical values under different terrain conditions and vegetation types; the statistical values include biomass statistical values and carbon storage statistical values; according to the terrain characteristic map and the vegetation type map, combined with the true distribution map, evaluating and analyzing the statistical values under different terrain conditions and vegetation types in the target area to obtain an analysis report on the biomass and carbon storage of the target area.

[0186] For example, in some specific embodiments, an analysis report can be provided based on the biomass and carbon storage distribution map generated by the inversion model, including distribution characteristics under different terrain and vegetation types and an evaluation of the inversion results. In some preferred embodiments, the following can be achieved:

[0187] S91. Biomass and carbon stock distribution maps generated , classify and analyze the terrain and vegetation types of the target area, and generate a terrain feature map of the target area and vegetation type maps ,in, Indicates the distribution characteristics of biomass and carbon storage under different terrain conditions, Indicates the distribution characteristics of biomass and carbon storage under different vegetation types;

[0188] S92, according to the terrain feature map and vegetation type maps , calculate biomass and carbon storage statistics under different terrain conditions and vegetation types and ,in, represents the sum of biomass and carbon storage under specific terrain conditions, Represents the sum of biomass and carbon storage under a specific vegetation type;

[0189] S93, combined with terrain feature map and vegetation type maps , evaluate the carbon storage inversion results of different terrain and vegetation types in the target area, and define the evaluation function :

[0190] ;

[0191] in, is the actual biomass and carbon storage data in the target area, and are weight functions of terrain and vegetation types, respectively, which are used to adjust the impact of different terrain and vegetation types on the evaluation results. Represent the relative error distribution of the inversion results and evaluate the accuracy of the inversion results under different conditions;

[0192] S94, based on the evaluation function , generate a biomass and carbon stock analysis report for the target area, which includes the distribution characteristics of biomass and carbon stocks under different terrains and vegetation types, evaluation of inversion results, relative error analysis, and recommendations for ecological monitoring and carbon stock management.

[0193] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0194] First of all, it should be noted that although existing technical solutions can provide data support for large-scale ecosystems by monitoring biomass and carbon stocks through drones equipped with hyperspectral sensors, they face multiple challenges when processing hyperspectral data. First, the high dimensionality and redundancy of the data increase the processing complexity, resulting in low computational efficiency and difficulty in achieving real-time performance. Second, traditional data processing methods are unable to effectively extract key feature information, especially in environments with complex terrain and diverse vegetation types. The inversion model has poor adaptability and cannot provide stable and accurate results. In addition, although the application of generative adversarial networks can generate distribution maps close to reality to a certain extent, its training process is complex and prone to overfitting, resulting in insufficient details in the results, which limits its application in carbon stock estimation.

[0195] The purpose of this invention is to address these shortcomings of the existing technology and propose a sparse representation-based method for inverting biomass carbon stocks using drone hyperspectral data to improve data processing efficiency and enhance the robustness and accuracy of the model. Through sparse representation technology, the redundancy of hyperspectral data is effectively reduced, key features are extracted, thereby reducing computational costs, and providing stable and accurate inversion results even under complex environmental conditions. This not only improves the practicality of drone hyperspectral technology in ecosystem monitoring, but also meets the requirements of ecological monitoring for efficiency, accuracy, and robustness, promoting the popularization and application of biomass and carbon stock monitoring.

[0196] like Figure 8 As shown, the present invention provides a method for inverting biomass carbon storage in a UAV hyperspectral region based on sparse representation, which may include the following steps:

[0197] S1. Using a drone equipped with a hyperspectral imaging sensor to fly over a target area, obtaining hyperspectral image data covering the target area, wherein the hyperspectral image data includes multi-band reflectance spectrum information of surface vegetation;

[0198] S2. Preprocessing of hyperspectral image data, including radiation correction, geometric correction and noise removal;

[0199] S3, by constructing a sparse basis dictionary, using a sparse coding algorithm to extract the most representative feature vectors in the hyperspectral image data, and performing dimensionality reduction processing while retaining the key spectral information in the hyperspectral image data;

[0200] S4. Constructing a primary generative adversarial network, wherein the primary generator of the primary generative adversarial network is used to generate a biomass and carbon storage distribution map corresponding to the hyperspectral image data after dimensionality reduction, and the primary discriminator is used to distinguish the generated biomass and carbon storage distribution map from the real biomass and carbon storage data. The primary generative adversarial network is trained through an adversarial process between the primary generator and the primary discriminator;

[0201] S5. Build an advanced generative adversarial network. The advanced generator of the advanced generative adversarial network further optimizes the generated results based on the biomass and carbon storage distribution maps generated by the primary generative adversarial network, making the biomass and carbon storage distribution maps more detailed and accurate. The advanced discriminator distinguishes the results generated by the primary generative adversarial network from the real data. The training of the advanced generative adversarial network is carried out through the adversarial process between the advanced generator and the advanced discriminator, so that the biomass and carbon storage distribution maps generated by the advanced generator are more consistent with the distribution characteristics of the real data.

[0202] S6. Optimize the generator and discriminator of the primary and advanced generative adversarial networks through joint training. The primary and advanced generative adversarial networks work together to optimize the generated biomass and carbon storage distribution maps through multiple rounds of iterative training.

[0203] S7. Based on the biomass and carbon storage distribution maps generated by the trained advanced generative adversarial network, an inversion model is constructed. The inversion model estimates the biomass and carbon storage of the target area by using the mapping relationship between the generated carbon storage biomass and carbon storage distribution maps and the hyperspectral feature vectors of the target area;

[0204] S8. Apply the inversion model to the hyperspectral image data of the target area to estimate the biomass and carbon stock distribution in the target area;

[0205] S9. Provide analysis reports based on the biomass and carbon storage distribution maps generated by the inversion model, including distribution characteristics under different terrain and vegetation types and evaluation of the inversion results.

[0206] In this embodiment, S1 includes the following sub-steps:

[0207] S11. Select a UAV flight path and altitude suitable for the target area, and determine the UAV flight speed v and flight altitude h, where the flight speed v should be adjusted according to the frame rate f and ground resolution r of the hyperspectral imaging sensor;

[0208] S12. Calculate the width W and length L of the ground surface that can be covered in a single flight based on the drone's flight altitude h and the sensor's field of view FOV:

[0209] ;

[0210] ;

[0211] in, and are the field of view angles of the hyperspectral imaging sensor in the horizontal and vertical directions, respectively;

[0212] S13. During the flight of the UAV, the multi-band reflectance spectrum data (multi-band reflectance spectrum information) collected by the hyperspectral imaging sensor is obtained in real time, and the spectral reflectance of each image pixel is recorded. ,in Indicated by wavelength The reflectivity at wavelengths ranging from visible light to near infrared , so that hyperspectral data can reflect the spectral characteristics of surface vegetation;

[0213] S14. Based on the flight path and time sequence of the UAV, the continuously acquired hyperspectral image data are spliced and registered to generate complete hyperspectral image data covering the target area. , where x and y are the surface coordinates, is the wavelength;

[0214] S15. In the acquired hyperspectral image data, calibrate the area and boundary of the surface vegetation and extract the effective area data containing multi-band reflectance spectrum information. ,in, Represents spectral data with biomass and carbon storage inversion significance in the target area.

[0215] In this embodiment, S3 includes the following sub-steps:

[0216] S31. Hyperspectral image data based on target area , build a sparse basis dictionary ,in, represents the i-th basis vector, k is the number of basis vectors in the dictionary, The build is optimized based on:

[0217] ;

[0218] in, is the optimized sparse basis dictionary, is the corresponding sparse coefficient, is a regularization parameter used to control the norm of the dictionary vector to ensure that the constructed dictionary can effectively represent the key spectral information in the hyperspectral data and reduce noise interference;

[0219] S32, using sparse coding algorithm Perform sparse representation, and for each hyperspectral pixel spectral vector X, obtain each spectral vector The sparse representation coefficient vector :

[0220] ;

[0221] in, and is the regularization parameter, is the sparse coefficient of the surrounding pixels, which is used to consider spatial consistency. This allows the generated sparse coefficient to not only reflect the local spectral characteristics but also consider the spectral consistency between regions, which is beneficial to the accuracy of the hyperspectral data collected by UAVs in the inversion of carbon storage in local areas.

[0222] S33, according to the sparse representation coefficient vector Extracting hyperspectral image data The most representative eigenvector in :

[0223] ;

[0224] in, is the smoothing parameter, x represents the spatial coordinates of the hyperspectral image data, that is, the position of the pixel in the image, that is, the horizontal coordinate, and y is the vertical coordinate of this point. Furthermore, x' means the position of the adjacent pixels, that is, the adjacent coordinates used when smoothing, and y' is the same. D* means the optimized sparse basis dictionary, is a weight function used to measure the similarity between adjacent pixels to ensure that when extracting the feature vector, not only the local key spectral information is retained, but also the data of different regions can be smoothly transitioned, thereby improving the representativeness and stability of the feature vector in the inversion process;

[0225] S34, yes All hyperspectral pixels in the image are sparsely coded and dimensionally reduced to generate sparse representation hyperspectral image data of the target area. ,in is the dimension-reduced feature vector of the j-th pixel, and m is the number of pixels in the hyperspectral image.

[0226] In this embodiment, if Figure 5 As shown, S4 includes the following sub-steps:

[0227] S41. Based on the hyperspectral image data Y after dimensionality reduction, a primary generative adversarial network is constructed. The primary generative adversarial network includes a primary generator and the primary discriminator ;

[0228] S42, Primary Generator Receive the reduced feature vector As input, generate corresponding biomass and carbon storage distribution maps , the goal of the primary generator is to learn the mapping function : , so that the generated biomass and carbon stock distribution maps are close to the real data;

[0229] S43, primary discriminator The input is the generated biomass and carbon stock distribution map and real biomass and carbon storage data The goal of the primary discriminator output is to distinguish the generated biomass and carbon storage distribution maps from the real data, that is, to learn a discriminant function : , where 1 represents real data and 0 represents generated data;

[0230] S44, through the primary generator and the primary discriminator The adversarial training process between them optimizes the parameters of the primary generator and the discriminator. The loss function of the primary generator is defined as:

[0231] ;

[0232] in, and Represents the real biomass and carbon storage data respectively And the eigenvector after dimensionality reduction The expectation on the primary discriminator The goal is to maximize the accuracy of discriminating real data, and the primary generator The goal is to minimize the ability of the primary discriminator to distinguish the generated data;

[0233] S45. Optimize the primary generator of the primary generative adversarial network through multiple rounds of iterative training and the primary discriminator The parameters of the generated biomass and carbon storage distribution maps are Visually and statistically close to real data .

[0234] In this embodiment, if Figure 6 As shown, S5 includes the following sub-steps:

[0235] S51. Biomass and carbon storage distribution maps generated based on primary generative adversarial networks , building an advanced generative adversarial network, which includes an advanced generator and high-level discriminators ;

[0236] S52, Advanced Generator Receive primary generated biomass and carbon stock distribution map As input, generate optimized biomass and carbon stock distribution maps , the goal of the advanced generator is to learn more complex mapping functions , optimize the biomass and carbon stock distribution maps at the detailed level, making them more refined and accurate;

[0237] S53, Advanced Discriminator The input is the biomass and carbon stock distribution map generated by the advanced generator and real biomass and carbon storage data , the discriminator output goal is to distinguish the biomass and carbon storage distribution maps generated by advanced methods from the real data, that is, to learn the discriminant function , where 1 represents real data and 0 represents generated data;

[0238] S54, through the advanced generator and high-level discriminators The adversarial training process between them optimizes the parameters of the high-level generator and the high-level discriminator. The loss function of the high-level generator is defined as:

[0239] ;

[0240] in, and Represents the real biomass and carbon storage data respectively and distribution maps of primary biomass and carbon storage Expectation on high-level discriminators The goal is to maximize the discrimination of real data, the advanced generator The goal is to minimize the ability of the high-level discriminator to distinguish high-level generated data;

[0241] S55. Optimize the advanced generator of the advanced generative adversarial network through multiple rounds of iterative training and high-level discriminators Parameters that make the advanced generator generate biomass and carbon stock distribution maps It is more consistent with the distribution characteristics of real data at the detailed level.

[0242] In this embodiment, S7 includes the following sub-steps:

[0243] S71. Biomass and carbon storage distribution maps generated by advanced generative adversarial networks , build an inversion model , the inversion model is used to generate the distribution map and the hyperspectral feature vector of the target area Establish a mapping relationship between them and estimate the biomass and carbon storage in the target area;

[0244] S72, Inversion Model The construction process includes defining the mapping function ,in, Represents the real biomass and carbon storage data of the target area by fitting the training samples;

[0245] S73. Biomass and carbon storage distribution maps generated using the least squares method Real data from the target area The error between them is defined as:

[0246] ;

[0247] Where N is the number of training samples, represents the Euclidean norm, represents the sparsity regularization term of the model parameters, is a regularization parameter used to control the complexity of the model and prevent overfitting.

[0248] In this embodiment, S8 includes the following sub-steps:

[0249] S81, the optimized inversion model Hyperspectral image data applied to the target area , according to the hyperspectral feature vector of each pixel , calculate biomass and carbon stock estimates for pixels :

[0250] ;

[0251] in, is a weighting function based on pixel position and wavelength, which is used to adjust the contribution of different spectral bands to biomass and carbon storage estimation. is the hyperspectral feature vector and sparse basis dictionary The similarity kernel function is used to capture the nonlinear relationship between local spectral features and carbon storage. is the sparse coding coefficient, which indicates the contribution of different basis vectors to a specific pixel. is the dictionary basis vector, and are the minimum and maximum wavelength ranges of the hyperspectral data, respectively;

[0252] S82, traverse all pixels in the target area, through the inversion model Generate complete biomass and carbon stock distribution maps :

[0253] ;

[0254] in, It is the weight matrix between pixel position and pixel number, which is used to integrate the estimation results of multiple pixels. is a smoothing function of adjacent pixels, used to perform spatial smoothing on biomass and carbon storage. is the standard deviation of the Gaussian kernel function, which controls the degree of smoothness, and Represents the coordinate range of the target area in the x and y directions respectively.

[0255] In this embodiment, S9 includes the following sub-steps:

[0256] S91. Biomass and carbon stock distribution maps generated , classify and analyze the terrain and vegetation types of the target area, and generate a terrain feature map of the target area and vegetation type maps ,in, Indicates the distribution characteristics of biomass and carbon storage under different terrain conditions, Indicates the distribution characteristics of biomass and carbon storage under different vegetation types;

[0257] S92, according to the terrain feature map and vegetation type maps , calculate biomass and carbon storage statistics under different terrain conditions and vegetation types and ,in, represents the sum of biomass and carbon storage under specific terrain conditions, Represents the sum of biomass and carbon storage under a specific vegetation type;

[0258] S93, combined with terrain feature map and vegetation type maps , evaluate the carbon storage inversion results of different terrain and vegetation types in the target area, and define the evaluation function :

[0259] ;

[0260] in, is the actual biomass and carbon storage data in the target area, and are weight functions of terrain and vegetation types, respectively, which are used to adjust the impact of different terrain and vegetation types on the evaluation results. Represent the relative error distribution of the inversion results and evaluate the accuracy of the inversion results under different conditions;

[0261] S94, based on the evaluation function , generate a biomass and carbon stock analysis report for the target area, which includes the distribution characteristics of biomass and carbon stocks under different terrains and vegetation types, evaluation of inversion results, relative error analysis, and recommendations for ecological monitoring and carbon stock management.

[0262] In some specific application scenarios, the technical solution of the present invention can be implemented as follows:

[0263] Taking a mountainous forest in southwest China as an example, the study area covers an area of about 100 square kilometers and has complex terrain, including mountains, hills and plains. It is covered by different types of vegetation, such as evergreen broad-leaved forests, coniferous forests and mixed forests. Due to the diverse vegetation types and complex terrain in the region, traditional biomass and carbon stock inversion methods are difficult to provide accurate estimation results in such a complex environment. The sparse representation-based UAV hyperspectral local area biomass carbon stock inversion method of the present invention was comprehensively tested and verified in the region to prove its effectiveness and accuracy under complex terrain and diverse vegetation types.

[0264] The research team set up multiple sample plots on different terrain types in the study area, totaling 50 plots. The area of each plot is about 1 hectare. As the collection points for training samples and verification samples, the drone is equipped with a hyperspectral imaging sensor. It performs multiple flight missions according to the preset flight path at different altitudes and terrain conditions, and collects hyperspectral image data covering the entire study area. The drone flies at an altitude of 300 meters and a speed of 10 meters per second. The spatial resolution of the sensor is 0.5 meters, and the spectral range covers 400 nanometers to 1000 nanometers, with a total of 200 bands.

[0265] After data collection is complete, the method of the present invention is used to process and analyze hyperspectral data. First, sparse representation technology is used to reduce the dimensionality of the collected hyperspectral data, a sparse basis dictionary is constructed, and the most representative feature vectors in the hyperspectral data are extracted using a sparse coding algorithm. Subsequently, a primary generative adversarial network is applied to generate preliminary biomass and carbon storage distribution maps. The generated results are further optimized using an advanced generative adversarial network, making the biomass and carbon storage distribution maps more detailed and accurate. Finally, based on the generated biomass and carbon storage distribution maps, an inversion model is constructed and applied to the entire study area, estimating the biomass and carbon storage distribution under various terrain and vegetation types within the study area.

[0266] To verify the effectiveness of the present invention, the research team conducted ground measurements of biomass and carbon storage in 50 sample plots and compared the measured data with the inversion results of the present invention and the traditional methods. The ground measurement data showed that the biomass of different vegetation types in the study area ranged from 100 tons per hectare to 350 tons per hectare, while the carbon storage ranged from 50 tons per hectare to 175 tons per hectare. The following Table 1 compares the biomass and carbon storage inversion results of the present invention and the traditional methods under different vegetation types:

[0267] Table 1

[0268]

[0269] It can be seen from the data in Table 1 above that the method of the present invention is significantly superior to the traditional method in the accuracy of biomass and carbon storage inversion, especially in complex terrain and diverse vegetation types. The inversion results of the traditional method have large deviations, especially in evergreen broad-leaved forests and mountain vegetation. The deviations between the inversion results of the traditional method and the measured data reach 30 tons per hectare and 25 tons per hectare, respectively. The method of the present invention, by combining sparse representation and generative adversarial network technology, effectively reduces data redundancy and captures the complex relationship between hyperspectral features and biomass and carbon storage, making the inversion results closer to the measured data, and the deviation from the measured data is controlled within 10 tons per hectare, greatly improving the accuracy of the inversion results.

[0270] In addition, the method of the present invention is also superior to traditional methods in data processing efficiency. The research team's test results show that when processing 100 square kilometers of hyperspectral data, the traditional method takes an average of about 12 hours. However, the method of the present invention effectively reduces the data dimension through sparse representation technology and optimizes the calculation process by combining generative adversarial network technology, reducing the total time to 8 hours. This greatly improves the efficiency of data processing and is of great significance for application scenarios that require real-time or near-real-time monitoring of biomass and carbon reserves.

[0271] The verification of this example proves the effectiveness and practicality of the present invention in complex terrain and diverse vegetation types. The biomass and carbon storage distribution map generated by the research team based on the method of the present invention can not only accurately reflect the biomass and carbon storage distribution in the study area, but also provide reliable data support for ecosystem management, carbon sink calculation and environmental protection.

[0272] In summary, the present invention significantly improves the efficiency and accuracy of biomass and carbon storage inversion by combining sparse representation, generative adversarial networks and drone hyperspectral imaging technology. It uses sparse representation technology to reduce the dimensionality of hyperspectral data, constructs a sparse basis dictionary that can effectively extract key spectral information, and combines it with a sparse coding algorithm to greatly reduce data redundancy and computational complexity, improve the speed of data processing, and retain important feature information in hyperspectral data, making the subsequent inversion process more efficient and accurate.

[0273] The present invention designs primary and advanced generative adversarial networks, which gradually optimize the generation quality of biomass and carbon storage distribution maps through a multi-level generative adversarial structure. The primary generative adversarial network generates preliminary biomass and carbon storage distribution maps through adversarial training between the generator and the discriminator. The advanced generative adversarial network further optimizes the generation results based on the primary generative adversarial network, making the distribution maps more detailed and accurate. The multi-level adversarial generation design greatly improves the adaptability of the inversion model to complex terrain and diverse vegetation types, making the generated biomass and carbon storage distribution maps more in line with actual conditions and having higher spatial resolution.

[0274] In the construction of the inversion model, the present invention establishes a nonlinear mapping relationship between the biomass and carbon storage distribution maps generated by the advanced generative adversarial network and the hyperspectral feature vectors of the target area, and optimizes the model parameters through the least squares method, effectively capturing the complex relationship between hyperspectral features and biomass and carbon storage, making the inversion results more accurate and robust, and able to provide stable and precise estimation results when dealing with diverse terrains and vegetation types.

[0275] On the other hand, Figure 9 As shown, an embodiment of the present invention provides a biomass carbon stock inversion device 900, which may include:

[0276] The first module 901 is used to obtain hyperspectral image data of a sample plot area in a target area; the hyperspectral image data includes multi-band reflectance spectral information of surface vegetation in the sample plot area; the sample plot area includes multiple sample plots of different terrain types in the target area;

[0277] The second module 902 is used to preprocess the hyperspectral image data; the preprocessing includes radiation correction, geometric correction and noise removal;

[0278] The third module 903 is used to construct a sparse basis dictionary based on the pre-processed hyperspectral image data and extract feature vectors using a sparse coding algorithm;

[0279] The fourth module 904 is configured to generate a first distribution map of biomass and carbon storage based on the feature vector using a preset first generative adversarial network;

[0280] A fifth module 905 is configured to generate a second distribution map of biomass and carbon storage based on the first distribution map using a preset second generative adversarial network;

[0281] Among them, the first generative adversarial network and the second generative adversarial network are constructed based on the adversarial training of the generator and the discriminator;

[0282] The sixth module 906 is configured to construct an inversion model based on the mapping relationship between the second distribution map and the eigenvector;

[0283] The seventh module 907 is used to apply the inversion model to the entire area of the target area to obtain a third distribution map of biomass and carbon storage in the target area.

[0284] In some embodiments, the apparatus may further include:

[0285] The eighth module is used to obtain the actual distribution map of biomass and carbon storage in the sample area;

[0286] A ninth module is configured to construct a first generator based on a first mapping function between the feature vector and the actual distribution graph;

[0287] A tenth module is configured to construct a first discriminator based on a first discriminant function for distinguishing a generated result of the first generator from an actual distribution graph;

[0288] An eleventh module is configured to generate a first training distribution graph through a first generator according to the feature vector;

[0289] A twelfth module is configured to generate a first discrimination result through a first discriminator according to the first training distribution map and the actual distribution map;

[0290] A thirteenth module is configured to perform a first adversarial training on the first generator and the first discriminator according to a preset first loss function based on the first discrimination result;

[0291] The fourteenth module is used to optimize the parameters of the first generator and the first discriminator according to the results of the first adversarial training; return to execute the eleventh module until the first number of iterations is reached to obtain the first generative adversarial network.

[0292] In some embodiments, the apparatus may further include:

[0293] The fifteenth module is used to obtain the actual distribution map of biomass and carbon storage in the sample area;

[0294] A sixteenth module is configured to construct a second generator based on a second mapping function between the first distribution map and the actual distribution map;

[0295] A seventeenth module is used to construct a second discriminator based on a second discriminant function that distinguishes the generated result of the second generator from the actual distribution graph;

[0296] An eighteenth module is configured to generate a second training distribution map through a second generator according to the first distribution map;

[0297] A nineteenth module is configured to generate a second discrimination result through a second discriminator according to the second training distribution map and the actual distribution map;

[0298] A twentieth module is configured to perform second adversarial training on the second generator and the second discriminator according to a preset second loss function based on the second discrimination result;

[0299] The twenty-first module is used to optimize the parameters of the second generator and the second discriminator according to the results of the second adversarial training; and return to execute the eighteenth module until the second number of iterations is reached to obtain the second generative adversarial network.

[0300] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0301] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor implements the above-described method for predicting the bottom boundary of the hydrate stability region when executing the computer program. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0302] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0303] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is shown. The electronic device 1000 includes:

[0304] The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention.

[0305] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

[0306] Input / output interface 1003, used to implement information input and output;

[0307] Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0308] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0309] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0310] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0311] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0312] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0313] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0314] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0315] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0316] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0317] It should be noted that although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0318] Through the above description of the embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes instructions for causing a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present invention.

[0319] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented by the present invention. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0320] Furthermore, while the present invention has been described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed in the present invention, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0321] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0322] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus (e.g., a computer-based apparatus, a processor-included apparatus, or other apparatus that can fetch and execute instructions from, an instruction execution apparatus, device, or apparatus). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus.

[0323] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0324] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0325] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0326] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0327] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A biomass carbon storage inversion method, characterized in that: The following steps are involved: Acquire hyperspectral image data of a sample plot area in a target area; the hyperspectral image data includes multi-band reflectance spectral information of surface vegetation in the sample plot area; the sample plot area includes a plurality of sample plots of different terrain types in the target area; Preprocessing the hyperspectral image data; the preprocessing includes radiation correction, geometric correction and noise removal; Constructing a sparse basis dictionary based on the pre-processed hyperspectral image data, and extracting a feature vector using a sparse coding algorithm; Based on the feature vector, a first distribution map of biomass and carbon storage is generated using a preset first generative adversarial network; Based on the first distribution map, a second distribution map of biomass and carbon storage is generated using a preset second generative adversarial network; The first generative adversarial network and the second generative adversarial network are constructed based on adversarial training of a generator and a discriminator; Constructing an inversion model based on a mapping relationship between the second distribution map and the characteristic vector; The inversion model is applied to the entire target area to obtain a third distribution map of biomass and carbon storage in the target area.

2. The biomass carbon stock inversion method according to claim 1, characterized in that: The method of obtaining hyperspectral image data of a sample area in a target area comprises the following steps: In response to a control instruction from a target object, a target UAV is controlled to fly to the sample plot area of the target area based on preset operating parameters; the target UAV is provided with a hyperspectral imaging sensor; Acquire the multi-band reflectance spectrum information collected by the hyperspectral imaging sensor during the flight of the target UAV in the sample plot area; According to the flight path and time series of the target UAV, all the multi-band reflectance spectrum information is spliced and registered to obtain the hyperspectral image data of the sample site area.

3. The biomass carbon stock inversion method according to claim 1, characterized in that: The process of constructing a sparse basis dictionary based on the pre-processed hyperspectral image data and extracting a feature vector using a sparse coding algorithm comprises the following steps: An initial sparse basis dictionary is constructed based on the preprocessed hyperspectral image data; the expression of the initial sparse basis dictionary is: ,in, represents the i-th basis vector, k is the number of basis vectors in the initial sparse basis dictionary; The initial sparse basis dictionary is optimized to obtain an optimized sparse basis dictionary; the expression of the optimized sparse basis dictionary is: ； in, represents the optimized sparse basis dictionary; represents the hyperspectral image data after the preprocessing, Represents the surface coordinates, Indicates wavelength; Represents the sparse coefficient corresponding to the i-th basis vector; is the regularization parameter; The sparse coding algorithm is used to perform sparse representation on the pre-processed hyperspectral image data to obtain a sparse representation coefficient vector of the spectral vector corresponding to each hyperspectral pixel; the expression of the sparse representation coefficient vector is: ； in, represents the sparse representation coefficient vector; Represents the spectral vector corresponding to the hyperspectral pixel; represents the initial sparse basis dictionary; Representing the sparse coefficient corresponding to the hyperspectral pixel; and is the regularization parameter; represents the sparse coefficient corresponding to the jth pixel around the hyperspectral pixel, and m is the number of pixels around the hyperspectral pixel; Extracting an initial feature vector based on the sparse representation coefficient vector and the optimized sparse basis dictionary; Sparse coding and dimensionality reduction are performed on the initial feature vector to obtain the feature vector.

4. The biomass carbon stock inversion method according to claim 1, characterized in that: The method further comprises the following steps: Obtaining actual distribution maps of biomass and carbon storage in the sample area; A first generator is constructed based on a first mapping function between the feature vector and the actual distribution graph; A first discriminant is constructed based on a first discriminant function that distinguishes the generated result of the first generator from the actual distribution graph; generating a first training distribution graph by the first generator according to the feature vector; generating a first discrimination result by the first discriminator according to the first training distribution map and the actual distribution map; Based on the first discrimination result, performing first adversarial training on the first generator and the first discriminator according to a preset first loss function; Optimize the parameters of the first generator and the first discriminator according to the results of the first adversarial training; return to execute the step of generating a first training distribution map through the first generator according to the feature vector until the first number of iterations is reached to obtain the first generative adversarial network.

5. The biomass carbon stock inversion method according to claim 1, characterized in that: The method further comprises the following steps: Obtaining actual distribution maps of biomass and carbon storage in the sample area; A second generator is constructed based on a second mapping function between the first distribution map and the actual distribution map; A second discriminant is constructed based on a second discriminant function that distinguishes the generated result of the second generator from the actual distribution graph; generating a second training distribution map by the second generator according to the first distribution map; generating a second discrimination result by the second discriminator according to the second training distribution map and the actual distribution map; Based on the second discrimination result, performing second adversarial training on the second generator and the second discriminator according to a preset second loss function; Optimize the parameters of the second generator and the second discriminator according to the results of the second adversarial training; return to the step of generating a second training distribution map through the second generator according to the first distribution map until the second number of iterations is reached to obtain the second generative adversarial network.

6. The biomass carbon stock inversion method according to claim 1, characterized in that: The constructing of an inversion model based on the mapping relationship between the second distribution map and the characteristic vector comprises the following steps: Obtaining actual distribution maps of biomass and carbon storage in the sample area; According to the mapping function between the characteristic vector and the actual distribution map, the mapping relationship between the second distribution map and the characteristic vector is optimized by using the least square method, and the inversion model is obtained by fitting.

7. The biomass carbon stock inversion method according to claim 1, characterized in that: The method further comprises the following steps: Obtaining a true distribution map of biomass and carbon storage in the target area; Based on the third distribution map, performing a classification analysis of the terrain and vegetation types of the target area to obtain a terrain feature map and a vegetation type map of the target area; Obtaining statistical values under different terrain conditions and vegetation types based on the terrain feature map and the vegetation type map; the statistical values include biomass statistical values and carbon storage statistical values; According to the terrain feature map and the vegetation type map, combined with the real distribution map, the statistical values under different terrain conditions and vegetation types in the target area are evaluated and analyzed to obtain an analysis report on the biomass and carbon storage of the target area.

8. A biomass carbon storage inversion device, characterized in that: include: The first module is used to obtain hyperspectral image data of the sample area in the target area; The hyperspectral image data includes multi-band reflectance spectrum information of surface vegetation in the sample plot area; the sample plot area includes a plurality of sample plots of different terrain types in the target area; The second module is used to preprocess the hyperspectral image data; the preprocessing includes radiation correction, geometric correction and noise removal; A third module is configured to construct a sparse basis dictionary based on the pre-processed hyperspectral image data and extract a feature vector using a sparse coding algorithm; A fourth module is configured to generate a first distribution map of biomass and carbon storage based on the feature vector using a preset first generative adversarial network; A fifth module is configured to generate a second distribution map of biomass and carbon storage based on the first distribution map using a preset second generative adversarial network; The first generative adversarial network and the second generative adversarial network are constructed based on adversarial training of a generator and a discriminator; A sixth module is configured to construct an inversion model based on a mapping relationship between the second distribution map and the eigenvector; The seventh module is used to apply the inversion model to the entire area of the target area to obtain a third distribution map of biomass and carbon storage in the target area.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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