Method for extracting field ridge center line based on high-resolution satellite image

Abstract

The application provides a field ridge center line extraction method based on high-resolution satellite images, and the method comprises the following steps: acquiring an original gray image; performing filtering processing on the original gray image to obtain an image texture feature map; performing image binarization processing, morphological processing, image thinning processing and post-processing on the image texture feature map to obtain the center line of the field ridge. The center line extraction method provided by the application is suitable for curved ridges with contour lines, and can improve the accuracy and processing efficiency of center line extraction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing satellite ground processing, and particularly relates to a field ridge center line extraction method based on high-resolution satellite images. BACKGROUND

[0002] With the rapid development of satellite remote sensing, the spatial resolution of satellite remote sensing data is higher and higher, and the remote sensing data is more and more widely applied to the field of agricultural information extraction.

[0003] A field is a landform, a land type, and a utilization status that can reflect consistency or relative consistency, and the field is usually surrounded by obvious natural boundaries such as roads and field ridges, or can maintain relative stability within a certain time range. Among them, different ridge directions require different degrees of light, wind, moisture, and soil fertility, such as the northwest, northeast, and coastal areas of China, the ridge direction is perpendicular to the wind direction to reduce wind damage, the ridge direction is perpendicular to the slope and along the contour line on high-slope land to prevent soil erosion, and the ridge direction is a basic feature for judging crop planting patterns and an auxiliary feature for understanding crop growth and yield. With the development of remote sensing technology, remote sensing data is more and more widely applied to the field of agricultural information extraction. Therefore, it is of great significance to use remote sensing data to quickly and accurately extract the ridge direction for agricultural production activities, and it is helpful for reasonable crop production planning.

[0004] The extraction of the field ridge center line is a technical prerequisite for obtaining the ridge direction. At present, some scholars have carried out related research. For example, Zheng Xiaolan et al. use Hough transform to determine the ridge line and realize the extraction of the field ridge center line for a cotton field image. However, Hough transform has the defects of large memory consumption and slow processing speed, and improper parameter selection may detect false straight lines. Jiang Hao et al. perform Laplace calculation and binarization on a panchromatic remote sensing image, and obtain a line center line by using an image thinning method, and finally obtain a straight line segment according to the curvature. It can be seen that in the prior art, the main object of the extraction of the field ridge center line is a straight ridge, and there is a lack of research on curved ridges planted along contour lines, and there is less research on the extraction of the field ridge center line combined with structural texture features. SUMMARY

[0005] The present application provides a field ridge center line extraction method based on high-resolution satellite images to solve the technical problems of inaccurate center line extraction in the prior art and a lack of research on curved ridges planted along contour lines. The present application aims to improve the accuracy and speed of field ridge center line extraction by extracting and processing curved ridges through texture features and morphological analysis.

[0006] In a first aspect, the present application provides a field ridge center line extraction method based on high-resolution satellite images, comprising:

[0007] Obtain the original grayscale image;

[0008] The original grayscale image is filtered to obtain an image texture feature map;

[0009] The image texture feature map is subjected to image binarization, morphological processing, image thinning, and post-processing to obtain the center line of the field ridge.

[0010] Furthermore, according to the method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by the present invention, the step of filtering the original grayscale image to obtain an image texture feature map includes:

[0011] The original grayscale image is filtered from four different directions to obtain four different filtering results;

[0012] The four different filtering results are added together to obtain the image texture feature map.

[0013] Furthermore, according to the method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by the present invention, the image binarization processing of the image texture feature map includes:

[0014] The image texture feature map is processed using the maximum inter-class variance method to determine the optimal threshold between the ridge class and the background class;

[0015] The image texture feature map is segmented according to the optimal threshold to obtain the optimal binarized image.

[0016] Furthermore, according to the method for extracting field ridge centerlines based on high-resolution satellite imagery provided by the present invention, the morphological processing of the image texture feature map includes:

[0017] The binarized image is subjected to erosion, dilation, opening, and closing operations to obtain a field ridge image.

[0018] Furthermore, according to the method for extracting field ridge centerlines based on high-resolution satellite imagery provided by the present invention, the image thinning process of the image texture feature map includes:

[0019] The field ridge image is refined using a topological approach to obtain a skeleton image of the field ridge centerline.

[0020] Furthermore, according to the method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by the present invention, the topological refinement of the field ridge image to obtain the skeleton image of the field ridge centerline includes:

[0021] Morphological erosion is used to remove the boundaries of the field ridge image, resulting in a skeleton image with the center line of the field ridge.

[0022] Furthermore, according to the method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by the present invention, the post-processing of the image texture feature map to obtain the centerline of the field ridge includes:

[0023] Geometric feature selection and line smoothing are performed on the skeleton image with the center line of the field ridge to obtain the center line of the field ridge.

[0024] Secondly, the present invention also provides a field ridge centerline extraction device based on high-resolution satellite imagery, comprising:

[0025] The acquisition module is used to acquire the original grayscale image;

[0026] The filtering module is used to filter the original grayscale image to obtain an image texture feature map;

[0027] The processing module is used to perform image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the center line of the field ridge.

[0028] Thirdly, the present invention also provides an electronic device, comprising:

[0029] Processor, memory, and bus, among which,

[0030] The processor and the memory communicate with each other via the bus;

[0031] The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the field centerline extraction method based on high-resolution satellite imagery as described in any of the preceding claims.

[0032] 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 field centerline extraction method based on high-resolution satellite imagery as described above.

[0033] This invention provides a method for extracting the centerline of field ridges based on high-resolution satellite imagery. The method includes: acquiring an original grayscale image; filtering the original grayscale image to obtain an image texture feature map; and performing image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the centerline of the field ridge. The centerline extraction method provided by this invention is applicable to curved ridges with contour lines and can improve the accuracy and processing efficiency of centerline extraction. Attached Figure Description

[0034] 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.

[0035] Figure 1 This is a flowchart illustrating the method for extracting the center line of a field ridge provided by the present invention;

[0036] Figure 2 This is a schematic diagram of the overall process of the field ridge centerline extraction method provided by the present invention;

[0037] Figure 3 This is an example diagram of the test area for the application of the field centerline extraction method provided by the present invention;

[0038] Figure 4 This is a schematic diagram of the original grayscale image that needs to be processed, provided by the present invention;

[0039] Figure 5 This is a schematic diagram of the original grayscale image after filtering, provided by the present invention.

[0040] Figure 6 This is a schematic diagram of the image binarization process provided by the present invention;

[0041] Figure 7 This is a schematic diagram provided by the present invention after morphological analysis;

[0042] Figure 8 This is a schematic diagram of the image after thinning processing provided by the present invention;

[0043] Figure 9 This is a schematic diagram of the image after post-processing provided by the present invention;

[0044] Figure 10 This is a schematic diagram of the centerline extraction results provided by the present invention;

[0045] Figure 11 This is a schematic diagram of the structure of the field ridge centerline extraction device provided by the present invention;

[0046] Figure 12 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] 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.

[0048] Figure 1 This is a flowchart illustrating the method for extracting field ridge centerlines based on high-resolution satellite imagery provided by the present invention, as shown below. Figure 1 As shown, this invention provides a method for extracting the center line of field ridges based on high-resolution satellite imagery, specifically including the following steps:

[0049] Step 101: Obtain the original grayscale image.

[0050] In this embodiment, it is necessary to obtain the original grayscale image taken by a high-resolution satellite, such as Google Earth high-resolution image data, etc. There is no specific limitation here, as long as it is high-resolution satellite image data.

[0051] Step 102: Filter the original grayscale image to obtain an image texture feature map.

[0052] In this embodiment, the acquired original grayscale image needs to be filtered to obtain image texture features. The filtering method can be Gabor filtering. The main idea of ​​Gabor filtering is that different textures generally have different center frequencies and bandwidths. Based on these frequencies and bandwidths, a set of Gabor filters can be designed to filter the texture image. It should be noted that in other embodiments, other filtering methods can also be used, which are not specifically limited here.

[0053] Step 103: Perform image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the center line of the field ridge.

[0054] In this embodiment, the obtained image texture feature map needs to be processed by image binarization, morphological processing, image thinning, and post-processing to obtain the center line of the field ridge. Among them, binarization (thresholding) is one of the simplest methods of image segmentation. Binarization can convert a grayscale image into a binary image, setting the grayscale of pixels greater than a certain threshold grayscale value as the grayscale maximum value, and setting the grayscale of pixels less than this value as the grayscale minimum value, thereby achieving binarization, which means presenting the entire image with a clear visual effect of only black and white.

[0055] Among them, morphological processing refers to mathematical morphology. Mathematical morphology is an image analysis discipline based on lattice theory and topology. It is the basic theory of mathematical morphological image processing. Its basic operations include: binary erosion and dilation, 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, gray value morphological gradient, etc.

[0056] It should be noted that the image thinning process involves extracting the center line of the field ridges, and the post-processing refers to smoothing or other processing of the center line of the field ridges. The specific processing methods are described in the following examples and will not be described in detail here.

[0057] The method for extracting the centerline of a field ridge based on high-resolution satellite imagery provided by this invention involves acquiring an original grayscale image; filtering the original grayscale image to obtain an image texture feature map; and performing image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the centerline of the field ridge. The centerline extraction method provided by this invention is applicable to curved ridges with contour lines, and can improve the accuracy and processing efficiency of centerline extraction.

[0058] Based on any of the above embodiments, in one embodiment, the step of filtering the original grayscale image to obtain an image texture feature map includes:

[0059] The original grayscale image is filtered from four different directions to obtain four different filtering results;

[0060] The four different filtering results are added together to obtain the image texture feature map.

[0061] In this embodiment, the original grayscale image needs to be filtered from four different directions to obtain four different filtering results. These four different filtering results are then summed to obtain the image texture feature map. For example, the original grayscale image can be filtered using Gabor filters in four directions (0°, 45°, 90°, and 135°), and the filtering results from different directions are then summed to obtain the final image texture feature map. The filtering results enhance the contrast between the ridges and the background, thereby enhancing the representation of the ridges.

[0062] The method for extracting centerlines of ridges based on high-resolution satellite imagery provided by this invention involves filtering the original grayscale image from four different directions to obtain four different filtering results. These four results are then summed to obtain an image texture feature map. This centerline extraction method is applicable to curved ridges with contour lines, improving the accuracy and processing efficiency of centerline extraction.

[0063] Based on any of the above embodiments, in one embodiment, the image binarization processing of the image texture feature map includes:

[0064] The image texture feature map is processed using the maximum inter-class variance method to determine the optimal threshold between the ridge class and the background class;

[0065] The image texture feature map is segmented according to the optimal threshold to obtain the optimal binarized image.

[0066] In this embodiment, the image texture feature map needs to be binarized using the maximum inter-class variance method. An adaptive thresholding method is employed to find a threshold that maximizes the inter-class variance between the ridge class and the background class, serving as the optimal threshold for segmentation and achieving preliminary extraction of the ridge target. The maximum inter-class variance method is a commonly used binarization method and an existing technology. After determining the maximum threshold, segmentation is performed based on this optimal threshold to obtain the best binarization result.

[0067] The method for extracting centerlines of field ridges based on high-resolution satellite imagery provided by this invention utilizes the maximum inter-class variance method to process the image texture feature map, determining the optimal threshold between the field ridge class and the background class. Then, the image texture feature map is segmented according to the optimal threshold to obtain an optimal binarized image. The centerline extraction method provided by this invention is applicable to curved ridges with contour lines, improving the accuracy and processing efficiency of centerline extraction.

[0068] Based on any of the above embodiments, in one embodiment, the morphological processing of the image texture feature map includes:

[0069] The binarized image is subjected to erosion, dilation, opening, and closing operations to obtain a field ridge image.

[0070] In this embodiment, the initially extracted binarized image of the field ridge target needs to be processed by erosion, dilation, opening and closing operations. Specifically, an opening operation is used to eliminate small objects in the binarized image, and a closing operation is performed to fill the small holes inside the field ridge, so that the patches on the same field ridge are as continuous as possible, and the patches between different field ridges are separated, thus obtaining the final field ridge image.

[0071] The method for extracting centerlines of field ridges based on high-resolution satellite imagery provided by this invention obtains field ridge images by performing erosion, dilation, opening, and closing operations on a binarized image. The centerline extraction method provided by this invention is applicable to curved ridges with contour lines, and can improve the accuracy and processing efficiency of centerline extraction.

[0072] Based on any of the above embodiments, in one embodiment, the image thinning process of the image texture feature map includes:

[0073] The field ridge image is refined using a topological approach to obtain a skeleton image of the field ridge centerline.

[0074] In this embodiment, a topology-based method is needed to refine the field ridge image, turning the ridges in the image into single-pixel, relatively continuous line patches. Then, morphological erosion is used to continuously remove the boundaries of the objects until only the skeleton of the center line of the field ridge image remains. This processing method is an iterative process, and the operation stops only after the skeleton of the remaining image center line has been processed.

[0075] The method for extracting centerlines of field ridges based on high-resolution satellite imagery provided by this invention refines the field ridge image using a topology-based approach to obtain a skeleton image of the field ridge centerline. This centerline extraction method is applicable to curved ridges with contour lines, improving the accuracy and processing efficiency of centerline extraction.

[0076] Based on any of the above embodiments, in one embodiment, the topology-based thinning of the field ridge image to obtain a skeleton image of the field ridge centerline includes:

[0077] Morphological erosion is used to remove the boundaries of the field ridge image, resulting in a skeleton image with the center line of the field ridge.

[0078] In this embodiment, the topology processing can be based on morphological erosion. This morphological erosion operation removes the boundaries of the field ridge image to obtain a skeleton image with the center line of the field ridge. Erosion is one of the most basic morphological operations, which can eliminate the boundary points of the image, causing the image to shrink inward along the boundary. It can also remove parts smaller than a specified structural element. Erosion is used to shrink or refine the foreground in a binary image, thereby achieving functions such as noise removal and element segmentation.

[0079] The method for extracting centerlines of field ridges based on high-resolution satellite imagery provided by this invention refines the field ridge image using a topology-based approach to obtain a skeleton image of the field ridge centerline. This centerline extraction method is applicable to curved ridges with contour lines, improving the accuracy and processing efficiency of centerline extraction.

[0080] Based on any of the above embodiments, in one embodiment, the post-processing of the image texture feature map to obtain the center line of the field ridge includes:

[0081] Geometric feature selection and line smoothing are performed on the skeleton image with the center line of the field ridge to obtain the center line of the field ridge.

[0082] In this embodiment, geometric feature selection and smoothing processing are required on the skeleton image with the center line of the field ridge to obtain the center line of the field ridge. The geometric feature selection includes raster to vector conversion processing, and removing short branch lines based on length attributes, retaining only the main line segments. The smoothing processing adopts Smooth Line smoothing to make the line segments as smooth as possible, and the polynomial approximation method PAEK with exponential kernel is used.

[0083] The method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by the present invention obtains the centerline of the field ridges by performing geometric feature selection and line smoothing processing on the skeleton image with the centerline of the field ridges, which can improve the accuracy and processing efficiency of the centerline extraction.

[0084] Based on any of the above embodiments, in one embodiment, such as Figure 2 As shown, the method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by this invention first extracts image texture feature maps in different directions through Gabor filtering to enhance the field ridge target. Second, it uses Otsu's method to perform image binarization processing to achieve preliminary extraction of the field ridge image. Third, it performs morphological processing using operations such as erosion, dilation, opening, and closing to eliminate as many voids as possible inside the field ridges and the adhesion between ridges as possible. Fourth, it uses a topology-based method to perform image thinning processing to extract the centerline skeleton of the field ridges. Fifth, it performs post-processing of the image through geometric feature selection and smoothing line algorithms to finally obtain the centerline of the field ridges.

[0085] The method for extracting the centerline of field ridges based on high-resolution satellite imagery provided by this invention mainly utilizes morphological operations, combined with Gabor filtering and smoothing line algorithms, to ultimately extract the centerline of the field ridges and improve the accuracy of centerline extraction.

[0086] Based on any of the above embodiments, in one embodiment, the field ridge centerline extraction method based on high-resolution satellite imagery provided by the present invention is used to extract the field ridge centerline. Figure 3 The image data of the experimental location shown is processed. The experimental study area is located in Xincun Township, Suihua City, Heilongjiang Province. The images were collected on October 4, 2019, using high-resolution Google Earth images. Then, the data is processed as follows... Figure 4The original grayscale image shown is processed with Gabor filtering. After Gabor filtering in four different directions, the contrast between the ridges and the background is enhanced to highlight the ridges, resulting in the image shown. Figure 5 The filtered result is shown in the figure.

[0087] It should be noted that in this embodiment, the filtered image also needs to undergo image binarization to achieve preliminary extraction of the field ridge image. For example... Figure 6 As shown, the image contains many burrs and internal voids, which need to be further eliminated.

[0088] It should be noted that in this embodiment, morphological processing is also required on the binarized image. A single opening operation is performed to eliminate small objects in the image, followed by a closing operation to fill in small holes inside the ridges, making the patches on the same ridge as continuous as possible while separating the patches between different ridges. The processing result is as follows: Figure 7 As shown.

[0089] It should be noted that in this embodiment, image thinning based on topology is also required to transform the field ridges into relatively continuous single-pixel line patches. Then, the image is converted into vector data, as shown in the figure. Figure 8 As shown, the initially extracted centerline still has small branches, requiring post-processing. Post-processing first removes shorter branches based on length attributes, retaining only the main line segments. Then, a Smooth Line processing method is used to smooth the line segments as much as possible. The resulting processed line is shown in the image. Figure 9 As shown, the final extraction result obtained in this embodiment is as follows: Figure 10 As shown.

[0090] Figure 11 This is a schematic diagram of the field ridge centerline extraction device based on high-resolution satellite imagery provided by the present invention, as shown below. Figure 11 As shown, the field ridge centerline extraction device based on high-resolution satellite imagery provided by the present invention includes:

[0091] The acquisition module 1101 is used to acquire the original grayscale image;

[0092] Filtering module 1102 is used to filter the original grayscale image to obtain an image texture feature map;

[0093] The processing module 1103 is used to perform image binarization, morphological processing, image thinning and post-processing on the image texture feature map to obtain the center line of the field ridge.

[0094] The field ridge centerline extraction device based on high-resolution satellite imagery provided by the present invention obtains an original grayscale image; filters the original grayscale image to obtain an image texture feature map; and performs image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the centerline of the field ridge. The centerline extraction device provided by the present invention is suitable for curved ridges with contour lines, and can improve the accuracy and processing efficiency of centerline extraction.

[0095] 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.

[0096] Figure 12 This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of the present invention, such as... Figure 12 As shown, the present invention provides an electronic device, including: a processor 1201, a memory 1202, and a bus 1203;

[0097] The processor 1201 and the memory 1202 communicate with each other via the bus 1203.

[0098] The processor 1201 is used to call the program instructions stored in the storage 1202 to execute the methods provided in the above method embodiments, such as: acquiring an original grayscale image; filtering the original grayscale image to obtain an image texture feature map; performing image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the center line of the field ridge.

[0099] Furthermore, the logical instructions in the aforementioned memory 1202 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, 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.

[0100] 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 execute the methods provided by the above methods, the method comprising: acquiring an original grayscale image; filtering the original grayscale image to obtain an image texture feature map; performing image binarization, morphological processing, image thinning processing and post-processing on the image texture feature map to obtain the center line of the field ridge.

[0101] 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: acquiring an original grayscale image; filtering the original grayscale image to obtain an image texture feature map; and performing image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the center line of the field ridge.

[0102] 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.

[0103] 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.

[0104] 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 extracting the centerline of field ridges based on high-resolution satellite imagery, characterized in that, include: Obtain the original grayscale image; The original grayscale image is filtered to obtain an image texture feature map; The image texture feature map is subjected to image binarization, morphological processing, image thinning, and post-processing to obtain the center line of the field ridges; The step of filtering the original grayscale image to obtain an image texture feature map includes: The original grayscale image is filtered from four different directions to obtain four different filtering results; The four different filtering results are added together to obtain the image texture feature map; The image binarization process for the image texture feature map includes: The image texture feature map is processed using the maximum inter-class variance method to determine the optimal threshold between the ridge class and the background class; The image texture feature map is segmented according to the optimal threshold to obtain the optimal binarized image; The morphological processing of the image texture feature map includes: The binarized image is subjected to erosion, dilation, opening, and closing operations to obtain a field ridge image.

2. The method for extracting the centerline of field ridges based on high-resolution satellite imagery according to claim 1, characterized in that, The image thinning process for the image texture feature map includes: The field ridge image is refined using a topological approach to obtain a skeleton image of the field ridge centerline.

3. The method for extracting the center line of a field ridge based on high-resolution satellite imagery according to claim 2, characterized in that, The topology-based method for refining the field ridge image to obtain the skeleton image of the field ridge centerline includes: Morphological erosion is used to remove the boundaries of the field ridge image, resulting in a skeleton image with the center line of the field ridge.

4. The method for extracting the center line of a field ridge based on high-resolution satellite imagery according to claim 3, characterized in that, The post-processing of the image texture feature map to obtain the center line of the field ridge includes: Geometric feature selection and line smoothing are performed on the skeleton image with the center line of the field ridge to obtain the center line of the field ridge.

5. A device for extracting the center line of a field ridge based on high-resolution satellite imagery, characterized in that, include: The acquisition module is used to acquire the original grayscale image; The filtering module is used to filter the original grayscale image to obtain an image texture feature map; The processing module is used to perform image binarization, morphological processing, image thinning, and post-processing on the image texture feature map to obtain the center line of the field ridge; The filtering module is used for: The original grayscale image is filtered from four different directions to obtain four different filtering results; The four different filtering results are added together to obtain the image texture feature map; The processing module is used for: The image texture feature map is processed using the maximum inter-class variance method to determine the optimal threshold between the ridge class and the background class; The image texture feature map is segmented according to the optimal threshold to obtain the optimal binarized image; The processing module is further configured to: The binarized image is subjected to erosion, dilation, opening, and closing operations to obtain a field ridge image.

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 call the program instructions to perform the steps of the field centerline extraction method based on high-resolution satellite imagery 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 field centerline extraction method based on high-resolution satellite imagery as described in any one of claims 1-4.

Images