A plot boundary identification method, device, electronic equipment, medium and product

Abstract

The application provides a plot boundary identification method and device, electronic equipment, medium and product. The method comprises the following steps: confirming preprocessed image data, performing first scale edge detection on the image data to obtain a plurality of first scale edge detection results; performing morphological analysis and second scale edge detection on the first scale edge detection results to obtain a plurality of second scale edge detection results; and integrating the first scale edge detection results and the second scale edge detection results to obtain plot data of the image data. The method provided by the application can realize fine detection of potential cultivated land edges through two-stage edge detection and morphological analysis, and improve the accuracy of plot boundary identification.

Description

Technical Field

[0001] This invention relates to the field of remote sensing satellite ground processing technology, and in particular to a method, apparatus, electronic device, medium and product for identifying land parcel boundaries. Background Technology

[0002] With the rapid development of satellite remote sensing, the spatial resolution of satellite remote sensing data is getting higher and higher, and remote sensing data is being used more and more widely in agricultural information extraction.

[0003] A plot of land refers to a piece of land that reflects a consistent or relatively consistent topography, land type, and current land use. Plots are typically surrounded by clear natural boundaries, such as roads or field ridges, or are arable land that can maintain relative stability within a certain timeframe. Accurate identification of plots helps improve the precision of crop identification, facilitating detailed monitoring of crop conditions at the plot scale. This includes precise guidance for quantitative irrigation and fertilization in crop growth monitoring; quantitative guidance for pesticide spraying in crop pest and disease monitoring; and guidance for the precise implementation of disaster mitigation measures and agricultural insurance claims in crop disaster monitoring. Furthermore, plot data is crucial for estimating crop yield per unit area, accurately estimating crop planting area, and estimating crop output.

[0004] In existing technologies, many scholars use image segmentation techniques to extract the edges of ground features. For example, Hu Tangao et al. used a method combining wavelet transform and watershed segmentation to extract land parcels from remote sensing images. They solved the over-segmentation problem by improving the region merging algorithm and finally extracted the boundaries of the land parcels using the Canny algorithm. Pang Xinhua et al. used Quickbird multispectral remote sensing images as their research object and extracted the edges of land parcels using morphological methods and boundary extraction techniques. Chen Yizhe et al. pointed out that although differential operators can effectively identify the edges of farmland parcels, they amplify noise. However, using a threshold method based on region segmentation can effectively eliminate the noise influence of farmland parcel images. Garcia et al. combined superpixel and supervised classification methods to optimize region merging, transforming the segmentation problem into a machine learning problem, and realized high-resolution remote sensing image boundary extraction in agricultural scenes. Wang Xiaojuan et al. extracted the boundaries of mountainous field roads by converting RGB images to HIS space and using a threshold segmentation method based on the I component. Wu Han et al. used UAV images as their research object and, within the framework of multi-scale combined segmentation methods, selected the optimal ground sampling distance and analyzed the boundary extraction accuracy with respect to scale variation curves to select the optimal segmentation scale, thereby realizing the extraction of land parcel edges. However, the above methods are suitable for simple shapes. For complex study areas, it is difficult to use these methods to perform threshold segmentation on the same scale and extract accurate plot boundaries using morphological methods. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, medium, and product for land parcel boundary identification to solve the technical problems of existing technologies that cannot identify land parcels in complex research areas and have low accuracy in land parcel boundary identification. This invention aims to improve the accuracy of land parcel boundary identification through two-level edge detection and morphological analysis.

[0006] In a first aspect, the present invention provides a method for identifying land parcel boundaries, comprising:

[0007] Confirm the preprocessed image data;

[0008] Perform edge detection at a first scale on the image data to obtain multiple edge detection results at the first scale.

[0009] Morphological analysis and edge detection at the second scale are performed on the edge detection results at the first scale to obtain multiple edge detection results at the second scale.

[0010] The first-scale edge detection results and the second-scale edge detection results are integrated to obtain the land parcel data of the image data;

[0011] Wherein, the first scale is larger than the second scale.

[0012] Furthermore, according to the land parcel boundary identification method provided by the present invention, the step of performing edge detection at a first scale on the image data to obtain multiple first-scale edge detection results includes:

[0013] Based on the Canny edge detection algorithm, the roads and field ridges in different areas of the image data are extracted at the first scale, resulting in multiple first-scale edge detection results.

[0014] Furthermore, according to the land parcel boundary identification method provided by the present invention, the morphological analysis and second-scale edge detection of the first-scale edge detection results yield multiple second-scale edge detection results, including:

[0015] Morphological processing is performed on the edge detection results at the first scale, and the morphologically processed edge detection results at the first scale are segmented to obtain multiple land parcels.

[0016] Morphological analysis and second-scale edge detection were performed on each land parcel to obtain multiple second-scale edge detection results.

[0017] Furthermore, according to the land parcel boundary identification method provided by the present invention, morphological analysis and second-scale edge detection are performed on each land parcel patch to obtain multiple second-scale edge detection results, including:

[0018] After performing morphological analysis on each plot, edge detection at the second scale is performed based on the spectral differences between field ridges and crops in different plots to identify different plots in each plot and obtain multiple edge detection results at the second scale.

[0019] Furthermore, according to the land parcel boundary identification method provided by the present invention, the step of integrating the first-scale edge detection results and the second-scale edge detection results to obtain the land parcel data of the image data includes:

[0020] By integrating the edge detection results at the first scale and the edge detection results at the second scale, complete farmland plot data is obtained.

[0021] The cultivated land parcel data is vectorized to obtain the parcel data of the image data.

[0022] Furthermore, according to the land parcel boundary identification method provided by the present invention, the confirmation of preprocessed image data includes:

[0023] Acquire raw image data;

[0024] The original image data is subjected to orthorectification, geometric correction, feature calculation, and bilateral filtering to obtain preprocessed image data.

[0025] Secondly, the present invention also provides a land parcel boundary identification device, comprising:

[0026] The confirmation module is used to confirm the preprocessed image data;

[0027] The first detection module is used to perform edge detection at a first scale on the image data to obtain multiple edge detection results at the first scale.

[0028] The second detection module is used to perform morphological analysis on the edge detection results at the first scale and edge detection at the second scale to obtain multiple edge detection results at the second scale.

[0029] An integration module is used to integrate the edge detection results at the first scale and the edge detection results at the second scale to obtain the land parcel data of the image data;

[0030] Wherein, the first scale is larger than the second scale.

[0031] Thirdly, the present invention also provides an electronic device, comprising:

[0032] Processor, memory, and bus, among which,

[0033] The processor and the memory communicate with each other via the bus;

[0034] The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the land parcel boundary identification method as described in any of the preceding items.

[0035] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the steps of the land parcel boundary identification method described above.

[0036] This invention provides a method, apparatus, electronic device, medium, and product for identifying land parcel boundaries. The method includes: confirming preprocessed image data; performing edge detection on the image data at a first scale to obtain multiple first-scale edge detection results; performing morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results; and integrating the first-scale and second-scale edge detection results to obtain land parcel data from the image data. The method provided by this invention, through two-level edge detection and morphological analysis, enables refined detection of potential farmland edges, improving the accuracy of land parcel boundary identification. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the land parcel boundary identification method provided by the present invention;

[0039] Figure 2 This is a schematic diagram of the overall process of the land parcel boundary identification method provided by the present invention;

[0040] Figure 3 This is an example diagram of the test area for land parcel boundary identification and processing provided by the present invention;

[0041] Figure 4 This is a schematic diagram of the first-scale edge detection of the test area provided by the present invention;

[0042] Figure 5 This is a schematic diagram of second-scale edge detection of the test area provided by the present invention;

[0043] Figure 6 This is a schematic diagram of the edge detection result integration processing provided by the present invention;

[0044] Figure 7 This is a schematic diagram of the edge detection results provided by the present invention;

[0045] Figure 8 This is one of the schematic diagrams of a partial edge detection result provided by the present invention;

[0046] Figure 9 This is a second schematic diagram of a partial edge detection result provided by the present invention;

[0047] Figure 10 This is a schematic diagram of the land boundary identification device provided by the present invention;

[0048] Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0050] Figure 1 This is a flowchart illustrating the land parcel boundary identification method provided by the present invention, as shown below. Figure 1 As shown, the present invention provides a method for identifying land parcel boundaries, specifically including the following steps:

[0051] Step 101: Confirm the preprocessed image data.

[0052] In this embodiment, the confirmed image data is data obtained through preprocessing. Preprocessing refers to correcting, filtering, and other processing of the image data. The specific processing procedure can be seen in the following embodiments, and will not be described in detail here.

[0053] Step 102: Perform edge detection on the image data at the first scale to obtain multiple edge detection results at the first scale.

[0054] In this embodiment, edge detection at a first scale needs to be performed on the preprocessed image data to obtain multiple edge detection results at the first scale. Here, the first scale refers to a large-scale area. Edge detection is a system for locating the edges of objects in a two-dimensional or three-dimensional image, mainly used to extract and divide the image data into multiple large regions with boundaries. It should be noted that the edge detection algorithm preferred in this embodiment is the Canny edge detection algorithm. This algorithm, developed by John F. Canny in 1986, is a multi-level edge detection algorithm with the goal of finding an optimal edge detection algorithm. The specific detection process is described in the following embodiment and will not be detailed here.

[0055] Step 103: Perform morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results.

[0056] In this embodiment, morphological analysis is also required on the edge detection results at the first scale, and then edge detection at the second scale is performed on the analysis results again. The second scale is smaller than the first scale, and the edge detection at the second scale is performed on the image after the edge detection at the first scale. For example, the edge detection at the first scale obtains the land parcel data of each region, while the edge detection at the second scale is to perform more detailed edge detection on each region.

[0057] Step 104: Integrate the edge detection results at the first scale and the edge detection results at the second scale to obtain the land parcel data of the image data; wherein, the first scale is larger than the second scale.

[0058] In this embodiment, the first-scale edge detection results and the second-scale edge detection results also need to be integrated. The second-scale edge detection results are added to the first-scale edge detection results to obtain the land parcel data from the image data. The specific integration process adopts a mature processing method in the prior art, which will not be described in detail here.

[0059] According to the land parcel boundary identification method provided by this invention, the preprocessed image data is confirmed, and edge detection at a first scale is performed on the image data to obtain multiple first-scale edge detection results; morphological analysis and second-scale edge detection are performed on the first-scale edge detection results to obtain multiple second-scale edge detection results; the first-scale edge detection results and the second-scale edge detection results are integrated to obtain the land parcel data of the image data. The method provided by this invention, through two-level edge detection and morphological analysis, can achieve refined detection of potential farmland edges and improve the accuracy of land parcel boundary identification.

[0060] Based on any of the above embodiments, in one embodiment, performing edge detection on the image data at a first scale to obtain multiple first-scale edge detection results includes:

[0061] Based on the Canny edge detection algorithm, the roads and field ridges in different areas of the image data are extracted at the first scale, resulting in multiple first-scale edge detection results.

[0062] In this embodiment, edge detection at the first scale is performed on the image data based on the Canny edge detection algorithm to extract roads of different levels and high-level field ridges within the region. In other words, edge detection at the large scale is performed on the image data to obtain multiple edge detection results at the first scale.

[0063] The land parcel boundary identification method provided by the present invention can improve the efficiency of land parcel boundary identification by performing large-scale identification on image data.

[0064] Based on any of the above embodiments, in one embodiment, performing morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results includes:

[0065] Morphological processing is performed on the edge detection results at the first scale, and the morphologically processed edge detection results at the first scale are segmented to obtain multiple land parcels.

[0066] Morphological analysis and second-scale edge detection were performed on each land parcel to obtain multiple second-scale edge detection results.

[0067] In this embodiment, the obtained first-scale edge detection results need to be morphologically processed. Then, the morphologically processed first-scale edge detection results are segmented to form large-scale land parcels. Each land parcel is then subjected to morphological analysis. Specifically, through morphological closing operations, holes in the land parcels are filled to obtain complete land parcels. That is, two morphological analyses are required: one before segmentation and another after segmentation of the resulting land parcels. It should be noted that this embodiment uses mathematical morphology. Mathematical morphology is an image analysis discipline based on lattice theory and topology. It is the fundamental theory of mathematical morphological image processing, and its basic operations include: binary erosion and dilation (morphological), binary opening and closing operations, skeleton extraction, limit erosion, hit-and-miss transformation, morphological gradient, Top-hat transformation, particle analysis, watershed transformation, gray-value erosion and dilation, gray-value opening and closing operations, and gray-value morphological gradients.

[0068] According to the land parcel boundary identification method provided by the present invention, by performing small-scale identification on image data, the land parcel boundaries in complex areas can be accurately identified, thereby improving the accuracy of land parcel boundary identification.

[0069] Based on any of the above embodiments, in one embodiment, morphological analysis and second-scale edge detection are performed on each land parcel to obtain multiple second-scale edge detection results, including:

[0070] After performing morphological analysis on each plot, edge detection at the second scale is performed based on the spectral differences between field ridges and crops in different plots to identify different plots in each plot and obtain multiple edge detection results at the second scale.

[0071] In this embodiment, after performing morphological analysis on each plot, fine small-scale edge detection is performed based on the small field ridges between different plots and the spectral differences of different crops, so as to accurately distinguish different plots within the plot.

[0072] According to the land parcel boundary identification method provided by the present invention, by performing small-scale identification on image data, the land parcel boundaries in complex areas can be accurately identified, thereby improving the accuracy of land parcel boundary identification.

[0073] Based on any of the above embodiments, in one embodiment, integrating the first-scale edge detection results and the second-scale edge detection results to obtain the land parcel data of the image data includes:

[0074] By integrating the edge detection results at the first scale and the edge detection results at the second scale, complete farmland plot data is obtained.

[0075] The cultivated land parcel data is vectorized to obtain the parcel data of the image data.

[0076] In this embodiment, the edge detection results at the first scale and the edge detection results at the second scale are integrated. Edge detection detects line segments, and polygons are generated from these lines, thus forming the land parcel data. The integrated results still need to be corrected and transformed to form complete land parcel data.

[0077] According to the land parcel boundary identification method provided by the present invention, by integrating the detection results and then vectorizing the integrated results, the land parcel data of the image data can be accurately determined, thereby improving the accuracy of land parcel boundary identification.

[0078] Based on any of the above embodiments, in one embodiment, confirming the preprocessed image data includes:

[0079] Acquire raw image data;

[0080] The original image data is subjected to orthorectification, geometric correction, feature calculation, and bilateral filtering to obtain preprocessed image data.

[0081] In this embodiment, the acquired raw image data needs to undergo image orthorectification, geometric fine correction, feature calculation, and bilateral filtering preprocessing. The main calculations involve NDVI (Normalized Difference Vegetation Index), a key parameter reflecting crop growth and nutrient information, and EVI (Enhanced Vegetation Index).

[0082] In this embodiment, the original image data also needs to be filtered using a bilateral filter. A bilateral filter is a non-linear filter that smooths images, considering not only the geometric proximity of pixels but also their grayscale differences. Therefore, this filter can smooth the image while preserving image edge information.

[0083] According to the land parcel boundary identification method provided by the present invention, by preprocessing the original image data, the land parcel data of the image data can be accurately determined, thereby improving the accuracy of land parcel boundary identification.

[0084] Based on any of the above embodiments, in one embodiment of the present invention, such as Figure 2 As shown, a multi-scale adaptive land parcel boundary detection method was constructed using Sentinel2 multispectral imagery. First, the traditional Canny edge detection operator was used to detect and integrate potential edges based on different features of the image data, forming potential edges. Second, the potential edges were used to perform initial segmentation of the original image, forming potential farmland parcel data at the first scale (larger scale). Third, morphological operations were used to fill in erroneously segmented parts of the potential parcels, forming potential parcel 2. Fourth, a finer-scale Canny operator was used on the potential parcels at the second scale (smaller scale) to perform finer-scale farmland edge detection, achieving refined segmentation of large areas and forming finer-scale parcel patch data. Fifth, the farmland parcel patch data was vectorized to generate farmland vector data. This scheme integrates two-level scale Canny edge detection operators and morphological operations to achieve refined detection of potential farmland edges.

[0085] Based on any of the above embodiments, in one embodiment, the experimental study area is located in northwestern Illinois, USA (within the corn and soybean growing belt), and the imagery was collected on July 8, 2018, using Sentinel-2 data. The specific location of the study area is as follows: Figure 3 As shown, after preprocessing and bilateral filtering, the original image data undergoes large-scale edge detection, which can detect roads of different grades and relatively obvious field ridges. The detection results are then morphologically processed and used for image segmentation to form large-scale land parcels, resulting in the following: Figure 4 The image shows a plot of land. The purpose of edge detection at the plot scale is to finely identify the more subtle boundaries within the plots. Compared to the first-scale (large-scale) edge detection results, the second-scale edge detection detects these finer boundaries, specifically as shown in the image. Figure 5 The detection results are shown below. Finally, by integrating the edge detection results at both scales and performing relevant morphological processing, the raster boundaries are transformed into vector plot data, as shown in the following example. Figure 6 As shown. Among them, Figure 7 The results of the land parcel boundary identification in the study area, Figure 8 This is a schematic diagram showing the result of part 1. Figure 9 This is a schematic diagram showing the results of local 2.

[0086] Figure 10 This is a schematic diagram of the land parcel identification device provided by the present invention, as shown below. Figure 10 As shown, the land parcel identification device provided by the present invention includes:

[0087] Confirmation module 1001 is used to confirm the preprocessed image data;

[0088] The first detection module 1002 is used to perform edge detection at a first scale on the image data to obtain multiple edge detection results at the first scale.

[0089] The second detection module 1003 is used to perform morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results.

[0090] The integration module 1004 is used to integrate the first-scale edge detection results and the second-scale edge detection results to obtain the land parcel data of the image data;

[0091] Wherein, the first scale is larger than the second scale.

[0092] According to the land parcel boundary identification device provided by the present invention, after confirming preprocessed image data, edge detection at a first scale is performed on the image data to obtain multiple first-scale edge detection results; morphological analysis and second-scale edge detection are performed on the first-scale edge detection results to obtain multiple second-scale edge detection results; the first-scale edge detection results and the second-scale edge detection results are integrated to obtain the land parcel data of the image data. The device provided by the present invention, through two-level edge detection and morphological analysis, can achieve refined detection of potential farmland edges, improving the accuracy of land parcel boundary identification.

[0093] Since the device described in this embodiment of the invention is based on the same principle as the method described in the above embodiments, more detailed explanations will not be repeated here.

[0094] Figure 11 This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of the present invention, such as... Figure 11 As shown, the present invention provides an electronic device, including: a processor 1101, a memory 1102 and a bus 1103;

[0095] The processor 1101 and the memory 1102 communicate with each other via the bus 1103.

[0096] The processor 1101 is used to call program instructions stored in 1102 to execute the methods provided in the above-described method embodiments, such as: confirming preprocessed image data; performing edge detection on the image data at a first scale to obtain multiple first-scale edge detection results; performing morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results; integrating the first-scale edge detection results and the second-scale edge detection results to obtain land parcel data of the image data; wherein the first scale is larger than the second scale.

[0097] Furthermore, the logical instructions in the aforementioned memory 1103 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part 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 the present invention. 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.

[0098] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to perform the methods provided by the above methods, the method comprising: confirming preprocessed image data; performing edge detection on the image data at a first scale to obtain multiple first-scale edge detection results; performing morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results; integrating the first-scale edge detection results and the second-scale edge detection results to obtain land parcel data of the image data; wherein the first scale is larger than the second scale.

[0099] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided above, the method comprising: confirming preprocessed image data; performing edge detection on the image data at a first scale to obtain multiple first-scale edge detection results; performing morphological analysis and second-scale edge detection on the first-scale edge detection results to obtain multiple second-scale edge detection results; integrating the first-scale edge detection results and the second-scale edge detection results to obtain land parcel data of the image data; wherein the first scale is larger than the second scale.

[0100] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying land parcel boundaries, characterized in that, include: Confirm the preprocessed image data; Perform edge detection at a first scale on the image data to obtain multiple edge detection results at the first scale. Morphological analysis and edge detection at the second scale are performed on the edge detection results at the first scale to obtain multiple edge detection results at the second scale. The first-scale edge detection results and the second-scale edge detection results are integrated to obtain the land parcel data of the image data; Wherein, the first scale is larger than the second scale; The morphological analysis and second-scale edge detection of the first-scale edge detection results yield multiple second-scale edge detection results, including: Morphological processing is performed on the edge detection results at the first scale, and the morphologically processed edge detection results at the first scale are segmented to obtain multiple land parcels. Morphological analysis and second-scale edge detection were performed on each land parcel to obtain multiple second-scale edge detection results. The morphological analysis and second-scale edge detection of each land parcel yielded multiple second-scale edge detection results, including: After performing morphological analysis on each plot, edge detection at the second scale is performed based on the spectral differences between field ridges and crops in different plots to identify different plots in each plot and obtain multiple edge detection results at the second scale.

2. The land parcel boundary identification method according to claim 1, characterized in that, The image data is subjected to edge detection at a first scale, resulting in multiple first-scale edge detection results, including: Based on the Canny edge detection algorithm, the roads and field ridges in different areas of the image data are extracted at the first scale, resulting in multiple first-scale edge detection results.

3. The land parcel boundary identification method according to claim 1, characterized in that, The process of integrating the first-scale edge detection results and the second-scale edge detection results to obtain the land parcel data from the image data includes: By integrating the edge detection results at the first scale and the edge detection results at the second scale, complete farmland plot data is obtained. The cultivated land parcel data is vectorized to obtain the parcel data of the image data.

4. The land parcel boundary identification method according to claim 1, characterized in that, The confirmation of preprocessed image data includes: Acquire raw image data; The original image data is subjected to orthorectification, geometric correction, feature calculation, and bilateral filtering to obtain preprocessed image data.

5. A land parcel boundary identification device, characterized in that, include: The confirmation module is used to confirm the preprocessed image data; The first detection module is used to perform edge detection at a first scale on the image data to obtain multiple edge detection results at the first scale. The second detection module is used to perform morphological analysis on the edge detection results at the first scale and edge detection at the second scale to obtain multiple edge detection results at the second scale. An integration module is used to integrate the edge detection results at the first scale and the edge detection results at the second scale to obtain the land parcel data of the image data; Wherein, the first scale is larger than the second scale; The morphological analysis and second-scale edge detection of the first-scale edge detection results yield multiple second-scale edge detection results, including: Morphological processing is performed on the edge detection results at the first scale, and the morphologically processed edge detection results at the first scale are segmented to obtain multiple land parcels. Morphological analysis and second-scale edge detection were performed on each land parcel to obtain multiple second-scale edge detection results. The morphological analysis and second-scale edge detection of each land parcel yielded multiple second-scale edge detection results, including: After performing morphological analysis on each plot, edge detection at the second scale is performed based on the spectral differences between field ridges and crops in different plots to identify different plots in each plot and obtain multiple edge detection results at the second scale.

6. An electronic device, characterized in that, include: Processor, memory, and bus, among which, The processor and the memory communicate with each other via the bus; The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the land parcel boundary identification method as described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the steps of the land parcel boundary identification method as described in any one of claims 1-4.

8. A computer program product, said computer program product comprising computer-executable instructions, characterized in that, When executed, the instructions are used to perform the steps of the land parcel boundary identification method as described in any one of claims 1 to 4.

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