Hyperspectral cadastral surveying and mapping method, system and equipment and storage medium

Abstract

The invention provides a hyperspectral cadastral surveying and mapping method, system and device and a storage medium. The hyperspectral cadastral surveying and mapping method comprises the following steps: acquiring a hyperspectral image of a specified region; determining a vector approaching degree of a spectral vector between every two pixel points, and further performing segmentation to obtain a plurality of image segmentation domains; for each image segmentation domain, obtaining reference spectrum values of all pixel points so as to obtain a spectrum similarity between each pixel point and the landform, determining a structural feature vector of each pixel point in the image segmentation domain through all the spectrum similarities, and obtaining all the structural feature vectors; obtaining all neighborhood pixel point sets, and determining the spectral difference entropy of each pixel point and the corresponding neighborhood pixel point set; the feature separation degree of each pixel point is determined through the structural feature vector and the spectral difference entropy of each pixel point, and segmentation is carried out based on all the feature separation degrees, so that cadastre labeling is realized. By adopting the scheme provided by the invention, the spectral redundancy in the hyperspectral image can be avoided, so that the capability of distinguishing the earth surface objects is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cadastral surveying and mapping, and more specifically, to a hyperspectral cadastral surveying and mapping method, system, device and storage medium. Background Art

[0002] Cadastral surveying involves the measurement and recording of land boundaries, areas, shapes and locations. Its main purpose is to ensure the clarity of land ownership, the legality of land use and the rational allocation of land resources. Cadastral surveying provides basic data support for land management departments, helping decision makers make scientific land planning and management decisions. It is of great significance to the effective management and sustainable use of land resources and is a key tool for achieving scientific management and decision-making.

[0003] Hyperspectral cadastral mapping is an advanced method that combines hyperspectral remote sensing technology and cadastral mapping, aiming to provide more detailed and accurate land information. This mapping technology uses hyperspectral sensors to obtain spectral data of the surface, thereby obtaining rich information about the type, state and characteristics of land objects. It mainly uses hyperspectral sensors to perform remote sensing measurements on the surface to obtain spectral information of each pixel. Among them, hyperspectral sensors can capture a large amount of spectral data in different spectral bands (usually from visible light to near-infrared regions). These data can reflect the spectral characteristics of surface materials. In the existing hyperspectral cadastral mapping process, the bands of hyperspectral images are often strongly correlated, resulting in excessive spectral correlation coefficients of hyperspectral images, causing spectral redundancy, which reduces the ability of classification algorithms to distinguish surface objects, thereby reducing the accuracy of cadastral mapping. Therefore, how to avoid spectral redundancy in hyperspectral images to improve the ability to distinguish surface objects has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a hyperspectral cadastral mapping method, system, device and storage medium, which can avoid spectral redundancy in hyperspectral images to improve the ability to distinguish surface objects.

[0005] In a first aspect, the present application provides a hyperspectral cadastral mapping method, comprising the following steps:

[0006] Start hyperspectral cadastral mapping to obtain hyperspectral images of designated areas;

[0007] Determine the vector approximation of the spectral vector between every two pixel points in the hyperspectral image, and perform approximate segmentation on the hyperspectral image according to all the vector approximations to obtain a plurality of image segmentation domains;

[0008] For each image segmentation domain, a reference spectral value of the image segmentation domain is obtained based on the topographic information of the specified area, and then the spectral similarity between each pixel point in the image segmentation domain and the topography of the specified area is determined according to the reference spectral value, and all the pixels in the image segmentation domain are feature constructed through all the spectral similarities to obtain the structural feature vector of each pixel point in the image segmentation domain, and then the structural feature vector of each pixel point in each image segmentation domain is obtained;

[0009] Obtaining a neighborhood pixel point set of each pixel point in the hyperspectral image, and determining a spectral difference entropy between each pixel point and the corresponding neighborhood pixel point set according to spectral distribution characteristics of the hyperspectral image;

[0010] The characteristic separation degree of each pixel in the hyperspectral image is determined by the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel. The hyperspectral image is segmented twice based on the characteristic separation degree of each pixel, and the designated area is marked according to the segmentation result.

[0011] In some embodiments, determining the vector proximity of the spectral vectors between every two pixels in the hyperspectral image specifically includes:

[0012] Selecting a pixel point in the hyperspectral image as a selected pixel point, and using the remaining pixel points in the hyperspectral image after selecting the selected pixel point as a pixel point transition set;

[0013] Determining the degree of vector proximity between the selected pixel point and the spectral vectors of each pixel point in the pixel point transition set;

[0014] Selecting a pixel point from all the pixel points in the pixel point transition set as a new selected pixel point, and using the remaining pixel points in the pixel point transition set after selecting the new selected pixel point as a new pixel point transition set;

[0015] Determine the vector closeness between the spectral vectors of each pixel point in the new selected pixel point and the new pixel point transition set;

[0016] The same process is repeated until all pixels in the hyperspectral image are selected, thereby obtaining the vector approximation of the spectral vector between every two pixels in the hyperspectral image.

[0017] In some embodiments, the hyperspectral image is approximately segmented according to all vector approximations to obtain multiple image segmentation domains, specifically including:

[0018] Determine a proximity matrix of the hyperspectral image according to the proximity of all vectors;

[0019] The hyperspectral image is segmented according to the similarity matrix to obtain a plurality of image segmentation domains.

[0020] In some embodiments, obtaining the reference spectrum value of the image segmentation domain based on the topographic information of the designated area specifically includes:

[0021] Determine the significant bands corresponding to the geomorphic information of the specified area;

[0022] Obtaining the significant spectral value of each pixel point in the image segmentation domain under the significant band;

[0023] The reference spectral value of the image segmentation domain is determined by all the significant spectral values.

[0024] In some embodiments, determining the spectral similarity between each pixel point in the image segmentation domain and the landform of the designated area according to the reference spectral value specifically includes:

[0025] Determine the characteristic spectral value of each pixel in the image segmentation domain;

[0026] Determine the standard spectrum value of each pixel point in the image segmentation domain by using the characteristic spectrum value of each pixel point and the reference spectrum value;

[0027] The spectral similarity between each pixel in the image segmentation domain and the landform in the specified area is determined based on all standard spectral values.

[0028] In some embodiments, constructing features for all pixels in the image segmentation domain through all spectral similarities to obtain a structural feature vector of each pixel in the image segmentation domain specifically includes:

[0029] Extract feature pixels from all pixels in the image segmentation domain through all spectral similarities;

[0030] Determine the inter-spectral change rate between each adjacent band in the spectral vector of the characteristic pixel point;

[0031] Extract multiple characteristic bands from all bands according to the change rates between each spectrum;

[0032] Select a pixel point in the image segmentation domain as the selected pixel point;

[0033] The spectral value of the selected pixel point in each characteristic band is constructed as a structural characteristic vector of the selected pixel point;

[0034] Continue to determine the structural feature vectors of the remaining pixels.

[0035] In some embodiments, determining the feature separation degree of each pixel in the hyperspectral image by using the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel specifically includes:

[0036] Select a pixel as the selected pixel;

[0037] Determine the feature variation according to the structural feature vector of the selected pixel point;

[0038] Determining the feature separation of the selected pixel point in the hyperspectral image according to the spectral difference entropy of the selected pixel point and the feature variability;

[0039] Continue to determine the feature separation of the remaining pixels in the hyperspectral image, so as to obtain the feature separation of each pixel in the hyperspectral image.

[0040] In a second aspect, the present application provides a hyperspectral cadastral mapping system, comprising:

[0041] The acquisition module is used to obtain the hyperspectral image of the specified area after starting the hyperspectral cadastral mapping;

[0042] A processing module, used for determining the vector approximation of the spectral vector between every two pixel points in the hyperspectral image, and performing approximate segmentation on the hyperspectral image according to all the vector approximations to obtain a plurality of image segmentation domains;

[0043] The processing module is further used to obtain, for each image segmentation domain, a reference spectral value of the image segmentation domain based on the landform information of the specified area, and then determine the spectral similarity between each pixel point in the image segmentation domain and the landform of the specified area according to the reference spectral value, perform feature construction on all the pixel points in the image segmentation domain through all the spectral similarities, obtain the structural feature vector of each pixel point in the image segmentation domain, and then obtain the structural feature vector of each pixel point in each image segmentation domain;

[0044] The processing module is further used to obtain a neighborhood pixel point set of each pixel point in the hyperspectral image, and determine a spectral difference entropy between each pixel point and a corresponding neighborhood pixel point set according to a spectral distribution feature of the hyperspectral image;

[0045] The execution module is used to determine the feature separation degree of each pixel in the hyperspectral image through the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, perform secondary segmentation on the hyperspectral image based on the feature separation degree of each pixel, and perform cadastral marking on the designated area according to the segmentation result.

[0046] In a third aspect, the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned hyperspectral cadastral mapping method when executing the computer program.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which implements the steps of the above-mentioned hyperspectral cadastral mapping method when executed by a processor.

[0048] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0049] In the hyperspectral cadastral mapping method, system, device and storage medium provided by the present application, after starting the hyperspectral cadastral mapping, a hyperspectral image of a specified area is obtained; the vector proximity of the spectral vector between every two pixel points in the hyperspectral image is determined, and the hyperspectral image is approximately segmented according to all the vector proximity to obtain a plurality of image segmentation domains; for each image segmentation domain, a reference spectral value of the image segmentation domain is obtained based on the landform information of the specified area, and then the spectral similarity between each pixel point in the image segmentation domain and the landform of the specified area is determined according to the reference spectral value, and all the pixel points in the image segmentation domain are approximated by all the spectral similarities. The invention relates to a method for constructing row features, obtaining the structural feature vector of each pixel in the image segmentation domain, and then obtaining the structural feature vector of each pixel in each image segmentation domain; obtaining the neighborhood pixel set of each pixel in the hyperspectral image, and determining the spectral difference entropy between each pixel and the corresponding neighborhood pixel set according to the spectral distribution characteristics of the hyperspectral image; determining the feature separation degree of each pixel in the hyperspectral image through the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, performing secondary segmentation on the hyperspectral image based on the feature separation degree of each pixel, and performing cadastral marking on the designated area according to the segmentation result.

[0050] It can be seen that in the present application, firstly, hyperspectral cadastral mapping is started, and after the hyperspectral image of the specified area is obtained, the hyperspectral image is segmented into multiple image segmentation domains by the vector closeness of the spectral vector between every two pixels in the hyperspectral image. Then, for each image segmentation domain, the representative spectral value (reference spectral value) for classification of the image segmentation domain is obtained based on the topographic information of the specified area, and then the matching degree (spectral similarity) between each pixel point in the image segmentation domain and the topography of the specified area is determined according to the reference spectral value. The most representative pixel point in the hyperspectral image is extracted through all the spectral similarities, and the key characteristic bands are selected based on the spectral change rate, and then the spectral values ​​under these characteristic bands are converted into the structural characteristic vectors of each pixel point, thereby avoiding The spectral values ​​on all bands are considered to avoid spectral redundancy caused by strong correlation between different bands. After obtaining the neighborhood pixel point set of each pixel point in the hyperspectral image, the degree of difference in spectral characteristics between each pixel point and the corresponding neighborhood pixel point set is determined, and then the distinguishing ability (feature separation degree) of each pixel point relative to its neighborhood in the hyperspectral image is determined according to the spectral value (structural feature vector) of each pixel point on all feature bands and the degree of difference in spectral characteristics between each pixel point and the corresponding neighborhood pixel point set, so as to perform secondary segmentation of the hyperspectral image with each feature separation degree, and perform cadastral annotation on the designated area according to the segmentation result; in summary, the scheme of the present application can avoid spectral redundancy in hyperspectral images to improve the ability to distinguish surface objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a schematic flow chart of a hyperspectral cadastral mapping method according to some embodiments of the present application;

[0052] Figure 2 is a schematic diagram of a process for determining a reference spectrum value according to some embodiments of the present application;

[0053] Figure 3 is a schematic diagram of a process for determining a feature separation degree according to some embodiments of the present application;

[0054] Figure 4 is a structural block diagram of a hyperspectral cadastral mapping system according to some embodiments of the present application;

[0055] Figure 5 It is a diagram of the internal structure of a computer device for implementing a hyperspectral cadastral mapping method according to some embodiments of the present application. DETAILED DESCRIPTION

[0056] The core of the present application is to obtain a hyperspectral image of a designated area after starting hyperspectral cadastral mapping; determine the vector proximity of the spectral vector between every two pixels in the hyperspectral image, and perform approximate segmentation of the hyperspectral image according to all vector proximity to obtain multiple image segmentation domains; for each image segmentation domain, obtain a reference spectral value of the image segmentation domain based on the topographic information of the designated area, and then determine the spectral similarity between each pixel in the image segmentation domain and the topography of the designated area according to the reference spectral value, perform feature construction on all pixels in the image segmentation domain through all spectral similarities, and obtain a structural feature vector of each pixel in the image segmentation domain. , and then obtain the structural feature vector of each pixel in each image segmentation domain; obtain the neighborhood pixel set of each pixel in the hyperspectral image, and determine the spectral difference entropy between each pixel and the corresponding neighborhood pixel set according to the spectral distribution characteristics of the hyperspectral image; determine the feature separation degree of each pixel in the hyperspectral image through the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, perform secondary segmentation on the hyperspectral image based on the feature separation degree of each pixel, and perform cadastral annotation on the designated area according to the segmentation result; spectral redundancy in the hyperspectral image can be avoided to improve the ability to distinguish surface objects.

[0057] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is a schematic flow chart of a hyperspectral cadastral mapping method according to some embodiments of the present application. The hyperspectral cadastral mapping method 100 mainly includes the following steps:

[0058] In step 101, hyperspectral cadastral mapping is started to obtain a hyperspectral image of a designated area.

[0059] In specific implementation, after starting hyperspectral cadastral mapping, a hyperspectral sensor can be installed on a drone device, and the drone can be flown over a designated area to collect hyperspectral information, and a hyperspectral image of the designated area can be constructed based on the collected information.

[0060] It should be noted that the hyperspectral sensor described in the present application is the Specim AisaFENIX model, which provides a band range of 400-2500nm and is suitable for imaging from visible light to short-wave infrared. In addition, the hyperspectral image is composed of multiple pixels, wherein each pixel corresponds to a spectral vector, and the spectral vector contains the spectral value of the corresponding pixel in all bands of the hyperspectral sensor.

[0061] In step 102, the vector proximity of the spectral vector between every two pixels in the hyperspectral image is determined, and the hyperspectral image is approximately segmented according to all the vector proximity to obtain a plurality of image segmentation domains.

[0062] In some embodiments, determining the vector proximity of the spectral vectors between every two pixels in the hyperspectral image may be achieved by using the following steps:

[0063] Selecting a pixel point in the hyperspectral image as a selected pixel point, and using the remaining pixel points in the hyperspectral image after selecting the selected pixel point as a pixel point transition set;

[0064] Determining the degree of vector proximity between the selected pixel point and the spectral vectors of each pixel point in the pixel point transition set;

[0065] Selecting a pixel point from all the pixel points in the pixel point transition set as a new selected pixel point, and using the remaining pixel points in the pixel point transition set after selecting the new selected pixel point as a new pixel point transition set;

[0066] Determine the vector closeness between the spectral vectors of each pixel point in the new selected pixel point and the new pixel point transition set;

[0067] The same process is repeated until all pixels in the hyperspectral image are selected, thereby obtaining the vector approximation of the spectral vector between every two pixels in the hyperspectral image.

[0068] In specific implementation, determining the vector closeness between the spectral vectors of the selected pixel point and each pixel point in the pixel point transition set can be achieved in the following manner, namely: first, determining the modulus length of the spectral vectors of the selected pixel point and all the pixel points in the pixel point transition set, then selecting a pixel point in the pixel point transition set as the selected transition pixel point, and secondly, dividing the dot product of the spectral vectors of the selected pixel point and the selected transition pixel point by the modulus length of the spectral vectors of the selected pixel point and the selected transition pixel point as the vector closeness between the selected pixel point and the selected transition pixel point, and continuing to determine the vector closeness between the selected pixel point and the remaining selected transition pixel points, thereby obtaining the vector closeness between the spectral vectors of the selected pixel point and all the pixel points in the pixel point transition set.

[0069] It should be noted that the vector proximity described in the present application represents the degree of similarity of the spectral characteristics between the corresponding two pixel points. The larger the vector proximity, the higher the degree of similarity of the spectral characteristics between the corresponding two pixel points. The smaller the vector proximity, the lower the degree of similarity of the spectral characteristics between the corresponding two pixel points.

[0070] In some embodiments, the hyperspectral image is approximately segmented according to all vector approximations to obtain multiple image segmentation domains, which can be achieved by the following steps:

[0071] Determine a proximity matrix of the hyperspectral image according to the proximity of all vectors;

[0072] The hyperspectral image is segmented according to the similarity matrix to obtain a plurality of image segmentation domains.

[0073] It should be noted that, in this application, after setting a unit matrix of size K*K with elements on the diagonal being 1, all vector proximity is filled into it, and the filled-in matrix is ​​used as the proximity matrix in this application, where K is the total number of pixels in the hyperspectral image, and in some embodiments, the proximity matrix is ​​expressed as:

[0074]

[0075] Wherein V is the proximity matrix, C(1, 2) represents the vector proximity between the 1st pixel and the 2nd pixel in the hyperspectral image, C(1, K-1) represents the vector proximity between the 1st pixel and the K-1th pixel in the hyperspectral image, and so on. The vector proximity in the 1st row and the Kth column in the proximity matrix is ​​expressed as C(1, K), and C(2, 1) represents the vector proximity in the 2nd row and the 1st column in the proximity matrix. And so on, the vector proximity in the K-1th row and the 1st column of the proximity matrix is ​​expressed as C(K-1, 1), and the vector proximity in the Kth row and the K-1th column is expressed as C(K, K-1).

[0076] It should be noted that the approximate segmentation in the present application refers to different image areas (i.e., image segmentation domains) divided according to the spectral characteristics of different pixels in the hyperspectral image. The approximate segmentation can realize the coarse classification of the hyperspectral image. In addition, the image segmentation domain characterizes different types of land objects in the hyperspectral image, such as vegetation, water bodies, bare soil, and urban buildings. In specific implementation, the hyperspectral image is segmented according to the similarity matrix to obtain multiple image segmentation domains, which can be implemented in the following manner, namely: first, the Laplace matrix of the proximity matrix is ​​determined, and then, after using the Laplace matrix as the similarity matrix of spectral clustering in the prior art, the Laplace matrix is ​​eigen-decomposed to obtain multiple eigenvectors, all the eigenvectors are classified, and each pixel in the hyperspectral image is segmented into multiple categories according to the classification results. Finally, the image composed of pixels of the corresponding category is used as an image segmentation domain, thereby obtaining multiple image segmentation domains.

[0077] In step 103, for each image segmentation domain, a reference spectral value of the image segmentation domain is obtained based on the topographic information of the specified area, and then the spectral similarity between each pixel point in the image segmentation domain and the topography of the specified area is determined according to the reference spectral value, and features are constructed for all pixel points in the image segmentation domain through all spectral similarities to obtain a structural feature vector of each pixel point in the image segmentation domain, and then the structural feature vector of each pixel point in each image segmentation domain is obtained.

[0078] In some embodiments, reference Figure 2 As shown in FIG. 1 , this figure is a schematic diagram of a process for determining reference spectral values ​​according to some embodiments of the present application. The reference spectral values ​​of the image segmentation domain can be obtained based on the topographic information of the specified area by using the following steps:

[0079] First, in 1031, a significant band corresponding to the landform information of the designated area is determined;

[0080] Then, in 1032, the significant spectral value of each pixel point in the image segmentation domain in the significant band is obtained;

[0081] Finally, in 1033, a reference spectral value of the image segmentation domain is determined through all the significant spectral values.

[0082] It should be noted that the landform information of the specified area refers to the landform type information of the specified area, such as different types such as water bodies, bare soil and vegetation, wherein each landform type will exhibit different spectral characteristics in the hyperspectral image, for example, the vegetation landform type has a higher reflectivity in the infrared band, and the water body landform type has a significant absorption rate in the short infrared band. The present application selects a band with a significant absorption rate and a higher reflectivity for the landform type of the specified area as a significant band corresponding to the landform information of the specified area. In other embodiments, other methods may be used to determine, which is not limited here.

[0083] It should be noted that the significant spectral value described in the present application refers to the spectral value of a pixel point under a specific significant band, and the significant band refers to a band that has a significant effect on the classification of land object types in the image segmentation domain. The reference spectral value is a representative spectral value extracted from the spectral data of all pixels in a specified area or image segmentation domain under the significant band, which is used for subsequent classification, comparison and analysis to help determine the spectral characteristics of a certain area and serve as a benchmark for comparing the spectral characteristics of the area with other areas; therefore, in specific implementation, obtaining the significant spectral values ​​of all pixels in the image segmentation domain under the significant band can be achieved in the following manner, namely: taking the spectral values ​​of all pixels in the image segmentation domain under the significant band as the significant spectral values ​​corresponding to each pixel point. In other embodiments, other methods can be used to determine, which will not be repeated here. In addition, as a preferred embodiment, determining the reference spectral values ​​of all pixels in the image segmentation domain through all significant spectral values ​​can be achieved in the following manner, namely: taking the standard deviation of the significant spectral values ​​corresponding to all pixels in the image segmentation domain as the reference spectral value of the image segmentation domain. In other embodiments, other methods can be used to determine, which will not be repeated here.

[0084] In some embodiments, determining the spectral similarity between each pixel point in the image segmentation domain and the landform of the designated area according to the reference spectral value can be achieved by using the following steps:

[0085] Determine the characteristic spectral value of each pixel in the image segmentation domain;

[0086] Determine the standard spectrum value of each pixel point in the image segmentation domain by using the characteristic spectrum value of each pixel point and the reference spectrum value;

[0087] The spectral similarity between each pixel in the image segmentation domain and the landform in the specified area is determined based on all standard spectral values.

[0088] In specific implementation, the characteristic spectral value of each pixel point in the image segmentation domain can be determined in the following manner, namely: the average value of the spectral value of each pixel point in the image segmentation domain in all bands is used as the characteristic spectral value of the corresponding pixel point. In other embodiments, other methods can also be used for determination, which is not limited here.

[0089] In specific implementation, the standard spectrum value of each pixel point in the image segmentation domain is determined by the characteristic spectrum value of each pixel point and the reference spectrum value, which can be implemented in the following manner, namely: the square root of the difference between the characteristic spectrum value of each pixel point and the reference spectrum value is used as the standard spectrum value of the corresponding pixel point. In other embodiments, other methods can be used to determine it, which is not limited here. As a preferred embodiment, the spectral similarity between each pixel point in the image segmentation domain and the landforms in the specified area is determined according to all standard spectrum values. The following method is implemented, namely: first, the maximum standard spectrum value and the minimum standard spectrum value among all standard spectrum values ​​are obtained, and secondly, after taking the maximum standard spectrum value and the minimum standard spectrum value as input, the normalization in the prior art is used to map the result corresponding to the characteristic spectrum value of each pixel point from 0 to 1, and after subtracting the value mapped from 0 to 1 of the result corresponding to the characteristic spectrum value of each pixel point from 1, the spectral similarity between each pixel point and the landforms in the specified area is obtained. In other embodiments, other methods can be used to determine it, which is not limited here.

[0090] It should be noted that the spectral similarity described in the present application characterizes the degree of match between the spectral characteristics of the pixel point and the topography of the specified area. The greater the spectral similarity, the higher the degree of match between the spectral characteristics of the pixel point and the topography of the specified area. The smaller the spectral similarity, the lower the degree of match between the spectral characteristics of the pixel point and the topography of the specified area. The value range of the spectral similarity is [0, 1].

[0091] In some embodiments, constructing features for all pixels in the image segmentation domain through all spectral similarities to obtain a structural feature vector for each pixel in the image segmentation domain can be achieved by the following steps:

[0092] Extract feature pixels from all pixels in the image segmentation domain through all spectral similarities;

[0093] Determine the inter-spectral change rate between each adjacent band in the spectral vector of the characteristic pixel point;

[0094] Extract multiple characteristic bands from all bands according to the change rates between each spectrum;

[0095] Select a pixel point in the image segmentation domain as the selected pixel point;

[0096] The spectral value of the selected pixel point in each characteristic band is constructed as a structural characteristic vector of the selected pixel point;

[0097] Continue to determine the structural feature vectors of the remaining pixels.

[0098] It should be noted that the characteristic pixel point refers to the pixel point corresponding to the highest spectral similarity. The present application can capture the most representative spectral information in the corresponding image segmentation domain by selecting the pixel point with the largest spectral similarity as the characteristic pixel point. Therefore, in the specific implementation, the characteristic pixel point can be extracted from all the pixels in the image segmentation domain through all the spectral similarities. The following method can be used, namely: first, the maximum spectral similarity is obtained from all the spectral similarities, and then the pixel point corresponding to the maximum spectral similarity is used as the characteristic pixel point. In other embodiments, other methods can also be used for determination, which is not limited here.

[0099] It should be noted that the inter-spectral change rate reflects the speed of change of the spectral vector of the characteristic pixel point between different bands, and can be used to identify and distinguish the spectral characteristics of different types of land objects. The larger the inter-spectral change rate, the greater the change in the spectral characteristics in the corresponding band area, and the smaller the inter-spectral change rate, the smaller the change in the spectral characteristics in the corresponding band area. As a preferred embodiment, determining the inter-spectral change rate between each adjacent band in the spectral vector of the characteristic pixel point can be achieved in the following manner, namely: first, obtaining the spectral values ​​of all bands in the spectral vector of the characteristic pixel point, and then dividing the result of subtracting the spectral values ​​between all adjacent bands by the absolute value of the difference between the adjacent bands. In order to correspond to the inter-spectral change rate between adjacent bands, other methods can be used to determine it in other embodiments, which is not limited here; as a preferred embodiment, extracting multiple characteristic bands from all bands according to each inter-spectral change rate can be achieved in the following way, namely: first, obtaining the maximum inter-spectral change rate among all the inter-spectral change rates, then obtaining multiple bands corresponding to the maximum inter-spectral change rate, and finally, using the band corresponding to the maximum inter-spectral change rate as the characteristic band in this application, thereby obtaining multiple characteristic bands. In addition, the characteristic band refers to the band corresponding to the maximum change in the spectral characteristics of the spectral vector of the characteristic pixel point, that is, analyzing the characteristic band can effectively identify the landform type.

[0100] It should be noted that the feature construction described in the present application refers to extracting the most representative pixel points in the hyperspectral image through all spectral similarities, and selecting key feature bands based on the spectral change rate, and then converting the spectral values ​​under these feature bands into structural feature vectors for each pixel point. The feature construction can improve the efficiency and accuracy of the model while reducing spectral redundancy. The structural feature vector refers to a vector constructed by constructing the spectral value of each pixel point in the selected feature band in the order of the feature bands, which represents the spectral characteristics of each pixel point in the most representative band (feature band). The present application analyzes the spectral characteristics of each pixel point in all feature bands through the structural feature vector, thereby avoiding spectral redundancy caused by strong correlation between different bands; as a preferred embodiment, constructing the spectral value of the selected pixel point in each feature band as the structural feature vector of the selected pixel point can be implemented in the following manner, namely: first, obtaining the spectral value of the selected pixel point in each feature band, and secondly, sorting all the spectral values ​​according to the size of the corresponding feature band as vector elements, thereby obtaining the structural feature vector of the selected pixel point.

[0101] In step 104, a neighborhood pixel point set of each pixel point in the hyperspectral image is obtained, and the spectral difference entropy between each pixel point and the corresponding neighborhood pixel point set is determined according to the spectral distribution characteristics of the hyperspectral image.

[0102] In specific implementation, obtaining the neighborhood pixel point set of each pixel point in the hyperspectral image can be achieved in the following manner, namely: first, select a pixel point as the selected pixel point, and then, based on the 8-way search method in the prior art, obtain 8 directions at the selected pixel point, namely, directly above, directly below, directly left, directly right, upper left, lower left, upper right and lower right, and then, select a pixel point adjacent to the selected pixel point in the corresponding direction as a neighborhood pixel point of the selected pixel point, thereby taking the obtained set of multiple neighborhood pixel points as the neighborhood pixel point set of the selected pixel point, and continue to determine the neighborhood pixel point sets of the remaining pixels, thereby obtaining multiple neighborhood pixel points corresponding to each pixel point. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0103] In some embodiments, determining the spectral difference entropy between each pixel point and the corresponding neighborhood pixel point set according to the spectral distribution characteristics of the hyperspectral image can be achieved by using the following steps:

[0104] Select a pixel as the selected pixel;

[0105] Determine the spectral angular distance between the selected pixel and each corresponding neighboring pixel point according to the spectral vector of the selected pixel point and the spectral vectors of each neighboring pixel point in the neighboring pixel point set corresponding to the selected pixel point, wherein all the spectral angular distances are the spectral distribution characteristics of the hyperspectral image;

[0106] Determine the relative distribution of the spectrum between the selected pixel point and each of the neighboring pixel points in the corresponding neighboring pixel point set according to all the spectral angular distances;

[0107] Determine the spectral difference entropy between the selected pixel and the corresponding set of neighboring pixel points through the relative distribution of all spectra;

[0108] Continue to determine the spectral difference entropy between the remaining pixels and the corresponding neighborhood pixel set.

[0109] In specific implementation, determining the spectral angular distance between the selected pixel and each corresponding neighboring pixel in the neighborhood pixel set corresponding to the selected pixel according to the spectral vector of the selected pixel and the spectral vectors of each neighboring pixel in the neighborhood pixel set corresponding to the selected pixel can be achieved in the following manner, namely: first, selecting a neighborhood pixel in the neighborhood pixel set corresponding to the selected pixel, then determining the modulus length of the spectral vector of the selected pixel and the modulus length of the spectral vector of the neighborhood pixel, secondly, determining the dot product of the spectral vector of the selected pixel and the spectral vector of the neighborhood pixel, and dividing the result by the product of the modulus length of the spectral vector of the selected pixel and the modulus length of the spectral vector of the neighborhood pixel, finally, taking the inverse cosine of the result as the spectral angular distance between the selected pixel and the neighborhood pixel, and repeating the above steps to determine the spectral angular distance between the selected pixel and the remaining neighborhood pixels in the corresponding neighborhood pixel set.

[0110] In specific implementation, the relative spectral distribution between the selected pixel and each neighboring pixel in the corresponding neighboring pixel set is determined based on all spectral angular distances. The method is as follows: first, the spectral angular distance corresponding to each neighboring pixel is taken as the negative exponent of the natural constant e. Then, all the results are summed. Secondly, a neighboring pixel in the neighboring pixel set corresponding to the selected pixel is selected. The quotient of the result corresponding to the neighboring pixel and the summed result is taken as the relative spectral distribution between the selected pixel and the neighboring pixel. The above steps are repeated to determine the relative spectral distribution between the selected pixel and the remaining neighboring pixel in the corresponding neighboring pixel set.

[0111] It should be noted that the relative spectral distribution characterizes the similarity between the spectral characteristics of the selected pixel point and each neighboring pixel point. The larger the relative spectral distribution, the higher the similarity between the spectral characteristics of the selected pixel point and each neighboring pixel point. The smaller the relative spectral distribution, the lower the similarity between the spectral characteristics of the selected pixel point and each neighboring pixel point.

[0112] It should be noted that the spectral difference entropy described in the present application represents the degree of confusion of all spectral values ​​in the spectral information around the selected pixel point, and the degree of confusion of all spectral values ​​can be used to reflect the difference of the spectral information around the selected pixel point. The larger the spectral difference entropy, the greater the difference in spectral characteristics between the selected pixel point and the neighboring pixel points, and the smaller the spectral difference entropy, the smaller the difference in spectral characteristics between the selected pixel point and the neighboring pixel points. As a preferred embodiment, the spectral difference entropy between the selected pixel point and the corresponding neighboring pixel point set is determined by all spectral relative distributions. The following method can be used to achieve this, namely: first, select the spectral relative distribution corresponding to a neighboring pixel point in the neighboring pixel point set corresponding to the selected pixel point, then multiply the spectral relative distribution by the logarithmic function value of the spectral relative distribution, repeat the above steps, determine the results corresponding to the remaining neighboring pixel points in the neighboring pixel point set, and finally, sum the results corresponding to all neighboring pixel points and take the negative result as the spectral difference entropy between the selected pixel point and the corresponding neighboring pixel point set.

[0113] In step 105, the characteristic separation degree of each pixel in the hyperspectral image is determined by the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, the hyperspectral image is secondary segmented based on the characteristic separation degree of each pixel, and the designated area is cadastral labeled according to the segmentation result.

[0114] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the feature separation degree according to some embodiments of the present application. The feature separation degree of each pixel in the hyperspectral image is determined by the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, which can be achieved by the following steps:

[0115] First, in 1051, a pixel is selected as a selected pixel;

[0116] Secondly, in 1052, the feature variability is determined according to the structural feature vector of the selected pixel point;

[0117] Then, in 1053, the feature separation degree of the selected pixel point in the hyperspectral image is determined according to the spectral difference entropy of the selected pixel point and the feature variability;

[0118] Finally, in 1054, the feature separation of the remaining pixel points in the hyperspectral image is continued to be determined, so as to obtain the feature separation of each pixel point in the hyperspectral image.

[0119] In specific implementation, determining the feature variability based on the structural feature vector of the selected pixel point can be achieved in the following manner, namely: taking the variance of all spectral values ​​in the structural feature vector of the selected pixel point as the feature variability of the selected pixel point. In other embodiments, other methods can also be used for determination, which are not limited here. In addition, as a preferred embodiment, determining the feature separation degree of the selected pixel point in the hyperspectral image based on the spectral difference entropy of the selected pixel point and the feature variability can be achieved in the following manner, namely: taking the result obtained by dividing the feature variability by the spectral difference entropy of the selected pixel point as the feature separation degree of the selected pixel point in the hyperspectral image.

[0120] It should be noted that the feature variability described in the present application characterizes the degree of difference between the spectral characteristics of the corresponding pixel points in different bands. The larger the feature variability, the higher the degree of difference between the spectral characteristics of the corresponding pixel points in different bands. The smaller the feature variability, the lower the degree of difference between the spectral characteristics of the corresponding pixel points in different bands. In addition, the feature separation is a quantitative indicator of the distinguishing ability of the corresponding pixel point relative to its neighborhood in the hyperspectral image. The larger the feature separation, the greater the difference between the spectral characteristics of the corresponding pixel point and its neighboring pixels, that is, it is easier to identify and classify. The smaller the feature separation, the smaller the difference between the spectral characteristics of the corresponding pixel point and its neighboring pixels, that is, it is not easy to identify and classify.

[0121] In some embodiments, the hyperspectral image is segmented twice based on the feature separation of each pixel point, and the cadastral marking of the designated area is performed according to the segmentation result, which can be achieved by the following steps:

[0122] The classification model is trained by the feature separation of each pixel;

[0123] Perform secondary segmentation on the hyperspectral image by using the classification model to obtain a category label for each pixel in the hyperspectral image;

[0124] The category labels of all pixel points are mapped back to the corresponding pixel positions of the hyperspectral image, so as to obtain a cadastral annotation map of the same size as the hyperspectral image, thereby realizing cadastral annotation of the designated area.

[0125] It should be noted that pixels with high feature separation are easier to distinguish from their neighbors, and the corresponding pixels represent important land features in the specified area. Therefore, the secondary segmentation of this application refers to using the scikit-learn library in Python to take the feature separation of each pixel as input, train a classification model, and classify all pixels in the hyperspectral image through the support vector machine in the prior art combined with the classification model to obtain the category label of each pixel in the hyperspectral image. The category label refers to the land feature type of the corresponding pixel, such as vegetation, water, bare soil, and urban buildings.

[0126] In addition, in another aspect of the present application, in some embodiments, the present application provides a hyperspectral cadastral mapping system, referring to Figure 4 , which is a structural block diagram of a hyperspectral cadastral mapping system according to some embodiments of the present application, the hyperspectral cadastral mapping system 200 includes: an acquisition module 201, a processing module 202 and an execution module 203, which are described as follows:

[0127] Acquisition module 201, in this application, acquisition module 201 is mainly used to acquire a hyperspectral image of a specified area after starting hyperspectral cadastral mapping;

[0128] The processing module 202 in the present application is mainly used to determine the vector proximity of the spectral vector between every two pixels in the hyperspectral image, and perform approximate segmentation on the hyperspectral image according to all the vector proximity to obtain multiple image segmentation domains;

[0129] In addition, the processing module 202 in the present application is also used to obtain a reference spectral value of the image segmentation domain based on the topographic information of the specified area for each image segmentation domain, and then determine the spectral similarity between each pixel point in the image segmentation domain and the topography of the specified area according to the reference spectral value, and construct features for all pixel points in the image segmentation domain through all spectral similarities to obtain a structural feature vector of each pixel point in the image segmentation domain, and then obtain a structural feature vector of each pixel point in each image segmentation domain;

[0130] In addition, the processing module 202 in the present application is also used to obtain a neighborhood pixel point set of each pixel point in the hyperspectral image, and determine the spectral difference entropy between each pixel point and the corresponding neighborhood pixel point set according to the spectral distribution characteristics of the hyperspectral image;

[0131] The execution module 203 in the present application is mainly used to determine the characteristic separation degree of each pixel in the hyperspectral image through the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, perform secondary segmentation on the hyperspectral image based on the characteristic separation degree of each pixel, and perform cadastral marking on the designated area according to the segmentation result.

[0132] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned hyperspectral cadastral mapping method.

[0133] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device using a hyperspectral cadastral mapping method according to some embodiments of the present application. The hyperspectral cadastral mapping method in the above embodiments can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0134] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the hyperspectral cadastral mapping method of the present application.

[0135] The communication bus 302 is used to transmit information between the above components.

[0136] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0137] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The hyperspectral cadastral mapping method in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0138] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0139] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0140] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0141] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned hyperspectral cadastral mapping method is implemented.

[0142] In summary, in the hyperspectral cadastral mapping method, system, device and storage medium disclosed in the embodiments of the present application, after starting hyperspectral cadastral mapping, a hyperspectral image of a specified area is obtained; the vector proximity of the spectral vector between every two pixel points in the hyperspectral image is determined, and the hyperspectral image is approximately segmented according to all vector proximity to obtain multiple image segmentation domains; for each image segmentation domain, a reference spectral value of the image segmentation domain is obtained based on the landform information of the specified area, and then the spectral similarity between each pixel point in the image segmentation domain and the landform of the specified area is determined according to the reference spectral value, and features are constructed for all pixel points in the image segmentation domain through all spectral similarities to obtain an image segmentation domain. The structural feature vector of each pixel point in the domain is obtained, and then the structural feature vector of each pixel point in each image segmentation domain is obtained; the neighborhood pixel point set of each pixel point in the hyperspectral image is obtained, and the spectral difference entropy between each pixel point and the corresponding neighborhood pixel point set is determined according to the spectral distribution characteristics of the hyperspectral image; the characteristic separation degree of each pixel point in the hyperspectral image is determined by the structural feature vector corresponding to each pixel point and the spectral difference entropy corresponding to each pixel point, the hyperspectral image is secondary segmented based on the characteristic separation degree of each pixel point, and the designated area is cadastral labeled according to the segmentation result; the spectral redundancy in the hyperspectral image can be avoided to improve the ability to distinguish surface objects.

[0143] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0144] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A hyperspectral cadastral mapping method, characterized in that: The steps include: Start hyperspectral cadastral mapping to obtain hyperspectral images of designated areas; Determine the vector approximation of the spectral vector between every two pixel points in the hyperspectral image, and perform approximate segmentation on the hyperspectral image according to all the vector approximations to obtain a plurality of image segmentation domains; For each image segmentation domain, a reference spectral value of the image segmentation domain is obtained based on the topographic information of the specified area, and then the spectral similarity between each pixel point in the image segmentation domain and the topography of the specified area is determined according to the reference spectral value, and all the pixels in the image segmentation domain are feature constructed through all the spectral similarities to obtain the structural feature vector of each pixel point in the image segmentation domain, and then the structural feature vector of each pixel point in each image segmentation domain is obtained; Obtaining a neighborhood pixel point set of each pixel point in the hyperspectral image, and determining a spectral difference entropy between each pixel point and the corresponding neighborhood pixel point set according to spectral distribution characteristics of the hyperspectral image; The characteristic separation degree of each pixel in the hyperspectral image is determined by the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel. The hyperspectral image is segmented twice based on the characteristic separation degree of each pixel, and the designated area is marked according to the segmentation result.

2. The method according to claim 1, characterized in that Determining the vector approximation of the spectral vector between every two pixels in the hyperspectral image specifically includes: Selecting a pixel point in the hyperspectral image as a selected pixel point, and using the remaining pixel points in the hyperspectral image after selecting the selected pixel point as a pixel point transition set; Determining the degree of vector proximity between the selected pixel point and the spectral vectors of each pixel point in the pixel point transition set; Selecting a pixel point from all the pixel points in the pixel point transition set as a new selected pixel point, and using the remaining pixel points in the pixel point transition set after selecting the new selected pixel point as a new pixel point transition set; Determine the vector closeness between the spectral vectors of each pixel point in the new selected pixel point and the new pixel point transition set; The same process is repeated until all pixels in the hyperspectral image are selected, thereby obtaining the vector approximation of the spectral vector between every two pixels in the hyperspectral image.

3. The method according to claim 1, characterized in that The hyperspectral image is approximately segmented according to all vector approximations to obtain multiple image segmentation domains, specifically including: Determine a proximity matrix of the hyperspectral image according to the proximity of all vectors; The hyperspectral image is segmented according to the similarity matrix to obtain a plurality of image segmentation domains.

4. The method according to claim 1, characterized in that The reference spectral value of the image segmentation domain is obtained based on the topographic information of the specified area, specifically including: Determine the significant bands corresponding to the geomorphic information of the specified area; Obtaining the significant spectral value of each pixel point in the image segmentation domain under the significant band; The reference spectral value of the image segmentation domain is determined by all the significant spectral values.

5. The method according to claim 1, characterized in that Determining the spectral similarity between each pixel point in the image segmentation domain and the landform in the designated area according to the reference spectral value specifically includes: Determine the characteristic spectral value of each pixel in the image segmentation domain; Determine the standard spectrum value of each pixel point in the image segmentation domain by using the characteristic spectrum value of each pixel point and the reference spectrum value; The spectral similarity between each pixel in the image segmentation domain and the landform in the specified area is determined based on all standard spectral values.

6. The method according to claim 1, characterized in that Through all spectral similarities, all pixels in the image segmentation domain are feature constructed to obtain the structural feature vectors of each pixel in the image segmentation domain, including: Extract feature pixels from all pixels in the image segmentation domain through all spectral similarities; Determine the inter-spectral change rate between each adjacent band in the spectral vector of the characteristic pixel point; Extract multiple characteristic bands from all bands according to the change rates between each spectrum; Select a pixel point in the image segmentation domain as the selected pixel point; The spectral value of the selected pixel point in each characteristic band is constructed as a structural characteristic vector of the selected pixel point; Continue to determine the structural feature vectors of the remaining pixels.

7. The method according to claim 1, characterized in that Determining the feature separation of each pixel in the hyperspectral image by using the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel specifically includes: Select a pixel as the selected pixel; Determine the feature variation according to the structural feature vector of the selected pixel point; Determining the feature separation of the selected pixel point in the hyperspectral image according to the spectral difference entropy of the selected pixel point and the feature variability; Continue to determine the feature separation of the remaining pixels in the hyperspectral image, so as to obtain the feature separation of each pixel in the hyperspectral image.

8. A hyperspectral cadastral mapping system, characterized in that: include: The acquisition module is used to obtain the hyperspectral image of the specified area after starting the hyperspectral cadastral mapping; A processing module, used for determining the vector approximation of the spectral vector between every two pixel points in the hyperspectral image, and performing approximate segmentation on the hyperspectral image according to all the vector approximations to obtain a plurality of image segmentation domains; The processing module is further used to obtain, for each image segmentation domain, a reference spectral value of the image segmentation domain based on the landform information of the specified area, and then determine the spectral similarity between each pixel point in the image segmentation domain and the landform of the specified area according to the reference spectral value, perform feature construction on all the pixel points in the image segmentation domain through all the spectral similarities, obtain the structural feature vector of each pixel point in the image segmentation domain, and then obtain the structural feature vector of each pixel point in each image segmentation domain; The processing module is further used to obtain a neighborhood pixel point set of each pixel point in the hyperspectral image, and determine a spectral difference entropy between each pixel point and a corresponding neighborhood pixel point set according to a spectral distribution feature of the hyperspectral image; The execution module is used to determine the feature separation degree of each pixel in the hyperspectral image through the structural feature vector corresponding to each pixel and the spectral difference entropy corresponding to each pixel, perform secondary segmentation on the hyperspectral image based on the feature separation degree of each pixel, and perform cadastral marking on the designated area according to the segmentation result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the hyperspectral cadastral mapping method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the hyperspectral cadastral mapping method according to any one of claims 1 to 7 are implemented.

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