A method, apparatus, storage medium, and electronic device for crop type identification

Abstract

This application proposes a crop type identification method, apparatus, storage medium, and electronic device, comprising: acquiring the crop phenological characteristic index corresponding to each pixel in the target area based on at least one set of target remote sensing image data; wherein the target remote sensing image data includes remote sensing image data corresponding to the target area; and determining the type identification result corresponding to each pixel in the target area based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area. This avoids a large amount of manual visual interactive interpretation and ground statistical survey work, and can quickly and accurately obtain the crop types in the target area.

Description

Technical Field

[0001] This application relates to the field of image processing, and more specifically, to a method, apparatus, storage medium, and electronic device for crop type identification. Background Technology

[0002] The area of ​​crop planting, or arable land, has always been an important factor in yield estimation and national food security. The estimation of regional crop planting area is also an important support for meeting the information needs of national macro-control decision-making and the trade decision-making needs of relevant grain enterprises. Especially now, due to the impact of factors such as ecological restoration of farmland, damage from natural disasters, and occupation by non-agricultural construction, the amount of arable land is decreasing year by year, making the acquisition of accurate, timely and reliable crop planting area information even more important.

[0003] Determining the planting area and spatial distribution of crops is a crucial aspect of monitoring crop planting information. The accuracy of these results highly depends on the accuracy of crop type identification. Therefore, improving the accuracy of crop type identification has become a challenging problem of concern to those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide a crop type identification method, apparatus, storage medium, and electronic device to at least partially improve the above-mentioned problems.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a crop type identification method, the method comprising:

[0007] Based on at least one set of remote sensing image data of the target, obtain the crop phenological period characteristic index corresponding to each pixel in the target area;

[0008] The target remote sensing image data includes the remote sensing image data corresponding to the target area;

[0009] Based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area, the type identification result corresponding to each pixel in the target area is determined.

[0010] Secondly, embodiments of this application provide a crop type identification device, the device comprising:

[0011] The processing unit is used to obtain the crop phenological characteristic index corresponding to each pixel in the target area based on at least one set of target remote sensing image data;

[0012] The target remote sensing image data includes the remote sensing image data corresponding to the target area;

[0013] The matching unit is used to determine the type identification result corresponding to each pixel in the target area based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area.

[0014] Thirdly, embodiments of this application provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method.

[0015] Fourthly, embodiments of this application provide an electronic device, the electronic device comprising: a processor and a memory, the memory being used to store one or more programs; when the one or more programs are executed by the processor, the above-described method is implemented.

[0016] Compared to existing technologies, the crop type identification method, apparatus, storage medium, and electronic device provided in this application include: acquiring the crop phenological characteristic index corresponding to each pixel in the target area based on at least one set of target remote sensing image data; wherein the target remote sensing image data includes remote sensing image data corresponding to the target area; and determining the type identification result corresponding to each pixel in the target area based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area. This avoids a large amount of manual visual interactive interpretation and ground statistical survey work, and can quickly and accurately obtain the crop types in the target area.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0020] Figure 2 A flowchart illustrating the crop type identification method provided in this application embodiment;

[0021] Figure 3 A schematic diagram of the sub-steps of S101 provided in the embodiments of this application;

[0022] Figure 4 This is a schematic diagram of a crop type identification device provided in an embodiment of this application.

[0023] In the diagram: 10-Processor; 11-Memory; 12-Bus; 13-Communication Interface; 201-Processing Unit; 202-Matching Unit. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] In the description of this application, it should be noted that the terms "upper", "lower", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0029] In the description of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0030] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0031] The inventors observed that phenological characteristic indices are relatively consistent for the same crop during the same phenological stage, and different crops may also have similar indices during a particular phenological stage. Crop phenological stages have a significant impact on planting structure identification, as differences in phenological stages lead to significant differences in the spatial distribution of crops. Existing crop planting structure classifications lack consideration of crop phenological characteristic indices, ignoring the spatial heterogeneity of crop phenological stages. This application introduces crop phenological characteristic indices (such as texture feature indices, pixel shape indices, normalized vegetation indices, enhanced vegetation indices, and red-edge position indices, etc.), combines them with field survey data of crop phenology, and applies a classification decision tree method to construct a method for identifying the spatial heterogeneity of typical crop planting structures. This avoids a large amount of manual visual interactive interpretation and ground statistical survey work, and can quickly and accurately obtain the crop types and planting areas of the target area.

[0032] Specifically, this application provides an electronic device, which may be a computer device, a server device, or a mobile phone device, etc. Please refer to... Figure 1 This is a schematic diagram of the structure of an electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected via the bus 12. The processor 10 is used to execute executable modules, such as computer programs, stored in the memory 11.

[0033] Processor 10 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the crop type identification method can be completed through integrated logic circuits in the hardware or software instructions within processor 10. The aforementioned processor 10 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0034] The memory 11 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0035] Bus 12 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Figure 1 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus 12 or one type of bus 12.

[0036] The memory 11 is used to store programs, such as programs corresponding to a crop type identification device. The crop type identification device includes at least one software functional module that can be stored in the memory 11 in the form of software or firmware, or embedded in the operating system (OS) of the electronic device. Upon receiving an execution instruction, the processor 10 executes the program to implement the crop type identification method.

[0037] Possibly, the electronic device provided in this application embodiment also includes a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus. The electronic device can receive quality data, requests, and satellite remote sensing data, etc., transmitted from other terminals through the communication interface.

[0038] It should be understood that, Figure 1The structure shown is only a partial schematic diagram of the electronic device; the electronic device may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0039] The crop type identification method provided in this application embodiment can be applied to, but is not limited to, [various applications]. Figure 1 For the specific process of the electronic devices shown, please refer to [link / reference]. Figure 2 The crop type identification methods include S101 and S102, which are described in detail below.

[0040] S101, based on at least one set of target remote sensing image data, obtain the crop phenological characteristic index corresponding to each pixel in the target area.

[0041] Among them, the target remote sensing image data includes the remote sensing image data corresponding to the target area.

[0042] As mentioned earlier, the phenological characteristic indices of the same crop are relatively consistent within the same phenological stage, and different crops may also have similar indices within a single phenological stage. If type identification is performed based solely on remote sensing image data of the target crop within a single phenological stage, misidentification may occur. To address this issue, multiple sets of remote sensing image data of the target crop can be obtained.

[0043] Optionally, at least one set of target remote sensing image data includes target remote sensing image data from N phenological periods, where N is greater than or equal to 2. It should be understood that different phenological periods occur at different temporal phases. When acquiring crop phenological characteristic indices, it is not limited to target remote sensing image data from a single phenological period; instead, based on target remote sensing image data from multiple phenological periods, the crop phenological characteristic index corresponding to each pixel in the target area is obtained. The obtained crop phenological characteristic index makes it easier to identify the type, thereby avoiding misidentification.

[0044] Optionally, to further improve the accuracy of the identification results, the target remote sensing image data can be preprocessed. Preprocessing includes image data preprocessing such as orthorectification, image registration, atmospheric correction, image fusion, image mosaicking, and image cropping.

[0045] S102, determine the type identification result corresponding to each pixel in the target area based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area.

[0046] Optionally, the sample area is the area where the sample plants are planted, and the sample area corresponds to at least one type of sample plant, such as corn, wheat, and cotton. The number of sample areas corresponding to each type of sample plant can be at least one. Optionally, survey points, i.e., sample areas, are set up based on the typical crop planting structure within the target area and according to the principles of typicality, representativeness, and uniformity, and the farmland plot information of the sample area is recorded.

[0047] It should be understood that before executing S102, the electronic device can acquire the crop phenological characteristic index corresponding to the sample area. Optionally, if the sample area belongs to the target area, the crop phenological characteristic index corresponding to the sample area can be selected from the crop phenological characteristic index corresponding to the target area based on the location information of the sample area. Optionally, if the sample area does not belong to the target area, the crop phenological characteristic index corresponding to the sample area can be directly acquired based on the remote sensing image data of the sample area.

[0048] The system matches the crop phenological characteristic indices corresponding to the target area and the sample areas. For example, it matches the crop phenological characteristic index corresponding to each pixel in the target area with the crop phenological characteristic index corresponding to each sample area, obtaining the matching degree between the pixel and each sample plant. Based on the obtained matching degree, the type identification result corresponding to each pixel in the target area is determined. This avoids a large amount of manual visual interactive interpretation and ground statistical survey work, and can quickly and accurately determine the crop types in the target area. It should also be noted that after determining the type identification result corresponding to each pixel in the target area, the planting area of ​​that type of crop can be determined based on the number of pixels of each type.

[0049] In summary, the crop type identification method provided in this application includes: acquiring the crop phenological characteristic index corresponding to each pixel in the target area based on at least one set of target remote sensing image data; wherein the target remote sensing image data includes remote sensing image data corresponding to the target area; and determining the type identification result corresponding to each pixel in the target area based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area. This method avoids a large amount of manual visual interactive interpretation and ground statistical survey work, and can quickly and accurately obtain the crop types in the target area.

[0050] exist Figure 2 Based on this, for the content in S101, this application embodiment also provides a possible implementation method, please refer to... Figure 3 S101 includes: S101-1, S101-2 and S101-3, which are described in detail below.

[0051] S101-1, based on at least one set of target remote sensing image data, obtain the texture feature index, pixel shape index, normalized vegetation index, enhanced vegetation index and red edge position index corresponding to each pixel in the target area.

[0052] S101-2 generates corresponding feature index raster data based on the texture feature index, pixel shape index, normalized vegetation index, enhanced vegetation index, and red edge position index corresponding to each pixel.

[0053] Feature index raster data refers to raster data with crop texture features, pixel shape features, normalized vegetation index features, enhanced vegetation index features, and red edge position features.

[0054] S101-3 uses a band synthesis tool to process the raster data of the characteristic index corresponding to each pixel to construct the crop phenological characteristic index corresponding to each pixel.

[0055] Band synthesis tools include, for example, the Layer Stacking tool in ENVI software. Optionally, texture feature indices include contrast texture index, correlation texture index, energy texture index, homogeneity texture index, and second-order entropy texture index. Texture feature indices are calculated using the gray-level co-occurrence matrix, which provides statistical attributes that quantitatively describe texture features based on the relationship between image gray-level direction, interval, and rate of change.

[0056] The formula for the texture feature index is:

[0057] Contrast ratio: Reflects the brightness contrast between a given pixel and its neighboring pixels. If pixels off-diagonal have large pixel values, meaning the image brightness changes rapidly, then FI... CON The value is relatively large. Contrast reflects the sharpness of an image and the depth of its texture grooves. The deeper the texture grooves, the greater the contrast and the clearer the visual effect; conversely, the lower the contrast, the shallower the grooves and the blurrier the effect.

[0058]

[0059] In the formula FI CON denoted as the contrast texture index, where m and n are the pixels with gray levels m and n, respectively, N is the total number of pixels, and p(m,n) is the probability that a pixel with gray level n will appear at a distance d from the pixel with gray level m in a direction θ.

[0060] Correlation: Represents the degree of similarity between pixels in the spatial gray-level co-occurrence matrix along the row or column direction. Therefore, the magnitude of the correlation value reflects the gray-level correlation of pixels in the image. When the gray-level values ​​of matrix pixels are uniformly equal, the correlation value is large; conversely, if the gray-level values ​​of matrix pixels differ greatly, the correlation value is small.

[0061]

[0062] In the formula FI COR σ is the relevance texture index, where m and n are the pixels with gray levels m and n, respectively, N is the total number of pixels, μm is the mean of gray level m, p(m,n) is the probability that a pixel with gray level n will appear at a distance d from pixel i in a direction θ, and σ is the probability of finding a pixel with gray level n. m Let σ be the variance of gray level m. n Let n be the variance of the gray level.

[0063] Energy: This is a measure of the amount of information contained in an image pixel, representing the complexity of texture in the image. The entropy value is large when all pixels in the co-occurrence matrix have the greatest randomness, all values ​​in the spatial co-occurrence matrix are almost equal, and the pixel distribution in the co-occurrence matrix is ​​relatively dispersed.

[0064]

[0065] In the formula FI ENE Let p(m,n) be the energy texture index, m and n be the pixels with gray levels m and n respectively, N be the total number of pixels, and p(m,n) be the probability that a pixel with gray level n will appear at a distance d from the pixel with gray level m in a direction θ.

[0066] Homogeneity is a measure that describes the uniformity of gray values ​​in an image, indicating whether the distribution of gray values ​​in the image is uniform. If the gray values ​​of pixels are the same or nearly similar, the homogeneity is high; if the gray value distribution of pixels is significantly uneven, the homogeneity is low.

[0067]

[0068] In the formula FI HOM denoted as the homogeneity texture index, where m and n are the pixels with gray levels m and n, respectively, N is the total number of pixels, and p(m,n) is the probability that a pixel with gray level n will appear at a distance d from the pixel with gray level m in a direction θ.

[0069] Second-order entropy: Represents the combined characteristics of the gray value at a pixel location and the gray value distribution of surrounding pixels, reflecting the degree of chaos in the image. The larger the second-order entropy value, the more chaotic and complex the image; the smaller the second-order entropy value, the more uniform and simpler the image.

[0070]

[0071] In the formula FI ENTLet p(m,n) be the second-order entropy texture index, m and n be the pixels with gray levels m and n respectively, N be the total number of pixels, p(m,n) be the probability that a pixel with gray level n appears at a distance d from a pixel with gray level m in a direction θ, and lg p(m,n) be the constant logarithm of p(m,n).

[0072] Optionally, the pixel shape index (PSI) is an index based on pixel spatial features. It describes the shape characteristics of the region surrounding a pixel, using the similarity between the central pixel and its neighbors to describe the shape distribution of the central pixel and its neighboring similar pixels. This effectively extracts spectral information from the original image and improves classification accuracy. The shape index of the central pixel refers to the total length of all lines pointing from that central pixel in all directions. The formula for the pixel shape index is:

[0073]

[0074] Among them, PSI m,n The pixel shape index characterizing the central pixel (m, n), where D is the number of directional lines, and d i Let be the length of the i-th direction line.

[0075] Optionally, any pixel can be the center pixel, where the center pixel is defined in relation to the pixel shape index of that pixel.

[0076] Optionally, the Normalized Difference Vegetation Index (NDVI) is one of the vegetation indices used to reflect vegetation health based on the high reflectance of vegetation in the near-infrared band and the strong absorption characteristics of chlorophyll in the red band. NDVI data can be sourced from Sentinel-2 L2A satellite products, with a temporal resolution of monthly crop growing seasons and a spatial resolution of 10m multispectral (including blue, green, red, and near-infrared bands). The formula for the NDVI is:

[0077]

[0078] Among them, NDVI i The normalized vegetation index (ρ) represents the pixel in the i-th month of the crop growing season. NIR The reflectance value of a pixel in the near-infrared band during the i-th month of the crop growing season; ρ RED The reflectance value of a pixel in the red light band during the i-th month of the crop growing season.

[0079] Optionally, the Enhanced Vegetation Index (EVI) is derived from the Normalized Difference Vegetation Index (NDVI), eliminating the effects of Rayleigh scattering and residual aerosols caused by atmospheric molecules, aerosols, water vapor, and ozone. The EVI data originates from Sentinel-2 L2A satellite products, with a temporal resolution of monthly crop growing seasons and a spatial resolution of 10m multispectral (including blue, green, red, and near-infrared bands). The formula for the EVI is:

[0080]

[0081] Among them, EVI i The enhanced vegetation index ρ represents the pixel in the i-th month of the crop growing season. NIR The reflectance value of a pixel in the near-infrared band during the i-th month of the crop growing season; ρ RED ρ represents the reflectance value of a pixel in the red band during the i-th month of the crop growing season. Blue The reflectance value of a pixel in the blue light band during the i-th month of the crop growing season is represented by C1 and C2, which are preset adjustment parameters, and L is a preset soil adjustment parameter. Optionally, C1 is a constant of 6, C2 is a constant of 7.5, and L is a constant of 1.

[0082] Optionally, the Red Edge Position Index (REP) is highly sensitive to changes in vegetation chlorophyll concentration, the broadening of absorption characteristics due to increased chlorophyll concentration, and the shift of the red edge towards longer wavelengths. The REP data is sourced from Sentinel-2 L2A satellite products, with a temporal resolution of monthly crop growing seasons and a spatial resolution resampling of 10m (including Bands 4, 5, 6, and 7). The formula for the REP is:

[0083]

[0084] Among them, REP i B4 represents the red edge position index of a pixel in the i-th month of the crop growing season, B5 represents the reflectance value of the pixel in the red light band (664.6nm) of Sentinel 2A data in the i-th month of the crop growing season, B6 represents the reflectance value of the pixel in the red edge band (704.1nm) of Sentinel 2A data in the i-th month of the crop growing season, B7 represents the reflectance value of the pixel in the red edge band (782.8nm) of Sentinel 2A data in the i-th month of the crop growing season, and B8 represents the reflectance value of the pixel in the red edge band (782.8nm) of Sentinel 2A data in the i-th month of the crop growing season.

[0085] exist Figure 2Based on this, for the content in S102, this application embodiment also provides an optional implementation method, please refer to the following.

[0086] Based on the crop phenological characteristic indices corresponding to the target region and the sample region, the spatial distribution of crop planting structure is obtained through the CART (Classification and Regression Tree) classification tree algorithm. The CART classification and regression tree can perform both classification and regression during the classification process; its output is the category of the classified sample, while the regression tree output is a real number.

[0087] The CART classification tree algorithm uses the Gini coefficient to select features. The Gini coefficient represents the impurity of the model. The smaller the Gini coefficient, the lower the impurity and the better the features.

[0088] The formula for calculating the Gini value is:

[0089]

[0090] In the formula, p(x) m ) is a category (x m Gini(D) represents the probability of two random samples in dataset D having different labels. Therefore, the smaller Gini(D) is, the higher the purity of the two samples.

[0091] Optionally, a CART classification regression tree can be used to obtain the probability that each pixel in the target region represents each sample crop, i.e., p(x m Based on this, the Gini value corresponding to each pixel can be obtained. When the Gini value is less than a preset threshold, p(x) can be... m The sample crop with the maximum value in the () is determined as the crop type corresponding to the pixel.

[0092] Optionally, the identification and interpretation results can be interactively optimized based on field surveys and statistical yearbooks, and the planting area of ​​typical crops can be statistically analyzed based on the optimization results.

[0093] Please see Figure 4 , Figure 4 The crop type identification device provided in this application embodiment may optionally be applied to the electronic device described above.

[0094] The crop type identification device includes a processing unit 201 and a matching unit 202.

[0095] Processing unit 201 is used to obtain the crop phenological period characteristic index corresponding to each pixel in the target area based on at least one set of target remote sensing image data;

[0096] Among them, the target remote sensing image data includes the remote sensing image data corresponding to the target area;

[0097] The matching unit 202 is used to determine the type identification result corresponding to each pixel in the target area based on the crop phenological characteristic index corresponding to the target area and the crop phenological characteristic index corresponding to the sample area.

[0098] It should be noted that the crop type identification device provided in this embodiment can execute the method flow shown in the above method flow embodiment to achieve the corresponding technical effects. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments.

[0099] This application also provides a storage medium storing computer instructions and programs, which, when read and run, execute the crop type identification method of the above embodiments. The storage medium may include memory, flash memory, registers, or a combination thereof.

[0100] The following provides an electronic device, which may be a computer device, a server device, a mobile phone device, etc., such as... Figure 1 As shown, the crop type identification method described above can be implemented. Specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 may be a CPU. The memory 11 is used to store one or more programs, which, when executed by the processor 10, perform the crop type identification method of the above embodiment.

[0101] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0102] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0103] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0105] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A crop type identification method characterized by, The method comprises: acquiring a crop phenological feature index corresponding to each pixel in the target region based on at least one set of target remote sensing image data; wherein the target remote sensing image data comprises remote sensing image data corresponding to the target region; determining a type recognition result corresponding to each pixel in the target region based on the crop phenological feature index corresponding to the target region and the crop phenological feature index corresponding to the sample region; the step of acquiring the crop phenological feature index corresponding to each pixel in the target region based on at least one set of target remote sensing image data comprises: acquiring a texture feature index, a pixel shape index, a normalized vegetation index, an enhanced vegetation index, and a red edge position index corresponding to each pixel in the target region based on at least one set of target remote sensing image data; generating corresponding feature index raster data based on the texture feature index, the pixel shape index, the normalized vegetation index, the enhanced vegetation index, and the red edge position index corresponding to each pixel; processing the feature index raster data corresponding to each pixel by a band synthesis tool to construct the crop phenological feature index corresponding to each pixel.

2. The crop type identification method of claim 1, wherein, The texture feature index comprises a contrast texture index, a correlation texture index, an energy texture index, a homogeneity texture index, and a second-order entropy texture index, and the formula of the texture feature index is: wherein FI CON denotes the contrast texture index, FI COR denotes the correlation texture index, FI ENE denotes the energy texture index, FI HOM denotes the homogeneity texture index, FI ENT denotes the second-order entropy texture index, m represents a pixel with a gray value of m, n represents a pixel with a gray value of n, and p(m, n) represents the probability of a pixel with a gray value of n occurring at a distance of d in a direction of θ from a pixel with a gray value of m.

3. The crop type identification method of claim 1, wherein, the formula of the pixel shape index is: wherein PSI m,n represents a pixel shape index of the center pixel (m, n), D is the number of direction lines, d i is the length of the i-th direction line.

4. The crop type identification method of claim 1, wherein, the formula of the normalized vegetation index is: wherein NDVI i NDVIi represents the normalized difference vegetation index of the pixel in the i-th month of the crop growing season, p NIR represents the reflectance value of the pixel in the near-infrared band in the i-th month of the crop growing season, p RED represents the reflectance value of the pixel in the red light band in the i-th month of the crop growing season.

5. The crop type identification method of claim 1, wherein, the formula of the enhanced vegetation index is: EVI i a reflectance value of the pixel in the near-infrared band in the i th month of the crop growth season; ρ NIR a reflectance value of the pixel in the near-infrared band in the i th month of the crop growth season; ρ RED a reflectance value of the pixel in the red light band in the i th month of the crop growth season; ρ Blue a reflectance value of the pixel in the blue light band in the i th month of the crop growth season; C1 and C2 are preset adjustment parameters, and L is a preset soil adjustment parameter.

6. The crop type identification method of claim 1, wherein, the formula of the red edge position index is: wherein REP i B4 represents the red edge position index of the pixel in the i th month of the crop growth season, B5 represents the red band reflectance value of the pixel in the i th month of the crop growth season, B6 represents the red edge band 6 reflectance value of the pixel in the i th month of the crop growth season, and B7 represents the red edge band 7 reflectance value of the pixel in the i th month of the crop growth season.

7. A crop type identification apparatus characterized by comprising: The device comprises: a processing unit configured to acquire a crop phenological feature index corresponding to each pixel in the target region based on at least one set of target remote sensing image data; wherein the target remote sensing image data comprises remote sensing image data corresponding to the target region; a matching unit configured to determine a type recognition result corresponding to each pixel in the target region based on the crop phenological feature index corresponding to the target region and the crop phenological feature index corresponding to the sample region; the step of acquiring the crop phenological feature index corresponding to each pixel in the target region based on at least one set of target remote sensing image data comprises: acquiring a texture feature index, a pixel shape index, a normalized vegetation index, an enhanced vegetation index, and a red edge position index corresponding to each pixel in the target region based on at least one set of target remote sensing image data; generating corresponding feature index raster data based on the texture feature index, the pixel shape index, the normalized vegetation index, the enhanced vegetation index, and the red edge position index corresponding to each pixel; and processing the feature index raster data corresponding to each pixel by a band synthesis tool to construct the crop phenological feature index corresponding to each pixel.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the method of any one of claims 1-6.

9. An electronic device, comprising: comprises: a processor and a memory for storing one or more programs; when the one or more programs are executed by the processor, the method of any one of claims 1-6 is implemented.

Images