A method, apparatus, equipment, and storage medium for water body detection in high-altitude and cold mountainous areas.

Abstract

This application provides a method, apparatus, device, and storage medium for detecting water bodies in high-altitude mountainous areas. The method includes: extracting the snow cover range from the target mountainous area's water body data based on true-color synthesis and color feature extraction methods from remote sensing data; extracting the mountain shadow range from the target mountainous area's water body data based on digital elevation model data; generating mask layers for the snow cover range and the mountain shadow range using dilation and erosion operations in image morphology; and removing the area covered by the mask layers from the target mountainous area's water body data to obtain the water body detection result for the target mountainous area. This method can accurately remove the interference from mountain shadows and snow cover noise during water body detection in high-altitude mountainous areas, thereby improving the accuracy of the water body detection results.

Description

Technical Field

[0001] This application relates to the field of water body detection, and more specifically, to a method, apparatus, equipment, and storage medium for detecting water bodies in high-altitude and cold mountainous areas. Background Technology

[0002] Currently, the detection of mountains and water bodies can only be carried out directly through remote sensing data. Satellite images of the target mountain area are acquired by satellite, and then water areas in the images are identified.

[0003] The above detection can only be considered a rough detection of water bodies in mountains, and it does not address the common problem of mountain shadows and snow noise interference in remote sensing results of surface water bodies in high-altitude and cold regions around the world.

[0004] Therefore, how to accurately remove the interference of mountain shadows and snow noise during water body detection in high-altitude and cold mountainous areas and improve the results of water body detection is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a method for detecting water bodies in high-altitude and cold mountainous areas. The technical solution of this application can accurately remove the interference of mountain shadows and snow noise during water body detection in high-altitude and cold mountainous areas, thereby improving the accuracy of water body detection results.

[0006] In a first aspect, embodiments of this application provide a method for detecting water bodies in high-altitude mountainous areas, including: extracting the snow cover range from the water body data of the target mountainous area based on a true-color synthesis and color feature extraction method of remote sensing data; extracting the mountain shadow range from the water body data of the target mountainous area based on digital elevation model data; generating a mask layer for the snow cover range and the mountain shadow range using dilation and erosion operations in image morphology; and removing the area covered by the mask layer from the water body data of the target mountainous area to obtain the water body detection result of the target mountainous area.

[0007] In the above embodiments, after extracting the snow cover range and the shadow range of the mountain through the extraction method, a mask layer of the snow cover range and the shadow range of the mountain can be generated by the application of the algorithm. Based on the existing target mountain water data, the water within the snow cover range and the shadow range of the mountain can be removed. This can accurately remove the noise interference from the mountain shadow and snow cover when detecting water bodies in high-altitude cold mountains, and improve the effect of water body detection.

[0008] In some embodiments, a mask layer for snow cover range and mountain shadow range is generated using dilation and erosion operations in image morphology, including: detecting water noise remaining in the target mountain water data in addition to snow cover range and mountain shadow range based on a neighborhood window detection method; removing the remaining water noise to obtain the mask layer.

[0009] In the above embodiments, this application can perform window processing on the mountain and water data to remove the water areas remaining in the mask layer, further eliminating water noise outside the snow cover area and the mountain shadow area, and obtaining a more accurate water area.

[0010] In some embodiments, based on the neighborhood window detection method, the detection of water noise remaining in the target mountain water data, excluding the snow cover area and the mountain shadow area, includes: dividing the water noise remaining in the target mountain water data, excluding the snow cover area and the mountain shadow area, into multiple windows of a preset size; using the neighborhood detection method to determine permanent water pixels in each of the multiple windows, and marking the permanent water pixels to obtain marked water data; if no permanent water pixels exist, then dividing the multiple windows into secondary windows again to obtain a second set of multiple windows; using the neighborhood detection method to determine permanent water pixels in each of the second set of multiple windows, and marking the permanent water pixels to obtain water detection results.

[0011] In the above embodiments, this application can mark water pixels for each window individually, accurately excluding potential water pixels within the mountain shadow area and snow cover area.

[0012] In some embodiments, the snow cover range in the target mountain water body data is extracted based on the remote sensing data true color synthesis and color feature extraction method, including: calculating the photometric component value of each pixel in the target mountain water body data in the color space; filtering pixels whose photometric component value is greater than a preset threshold; calculating the frequency of pixels whose photometric component value is greater than the preset photometric threshold being classified as snow cover in historical periods; and filtering pixels whose frequency of snow cover is greater than or equal to a preset probability threshold to obtain the snow cover range.

[0013] In the above embodiments, this application can accurately delineate the snow cover range in the target mountain area image by using the photometric component value calculation method to determine the pixel corresponding to the photometric value range where the snow covers is located.

[0014] In some embodiments, extracting the mountain shadow range from the water body data of the target mountain area based on digital elevation model data includes: converting the digital elevation model data into slope to obtain multiple slope values; calculating the slope corresponding to the multiple slope values ​​to obtain multiple slope values; filtering the slope values ​​that are greater than or equal to a preset slope threshold from the multiple slope values; and extracting the mountain area where the slope values ​​that are greater than or equal to the preset slope threshold are located from the multiple slope values ​​to obtain the mountain shadow range.

[0015] In the above embodiments, this application calculates the slope of each point in the target mountain area using digital elevation model data, and takes the mountain area with a slope greater than or equal to a preset slope threshold as the mountain shadow range, thus accurately dividing the mountain shadow range in the target mountain image.

[0016] In some embodiments, a mask layer for the snow cover area and the mountain shadow area is generated using dilation and erosion operations in image morphology, including: performing dilation, erosion and closing operations on the snow cover area and the mountain shadow area to fill the gaps and interrupted areas of the snow cover area and the mountain shadow area, thereby obtaining the mask layer.

[0017] In the above embodiments, this application can fill the gaps and interruptions in the undetected snow cover and mountain shadow areas through dilation, erosion, and closing operations, and accurately obtain the mask layers corresponding to the snow cover and mountain shadow areas.

[0018] In some embodiments, before extracting the snow cover range from the water body data of the target mountain area using the method of true color synthesis and color feature extraction based on remote sensing data, the method further includes: integrating the remote sensing surface reflectance data of the target mountain area year by year to obtain the water body data of the target mountain area, wherein the water body data of the target mountain area can be one or more.

[0019] In the above embodiments, this application can filter target mountain water body data for detection by using years of remote sensing surface reflectance data of the target mountain area, so as to enable accurate water body detection.

[0020] Secondly, embodiments of this application provide a device for detecting water bodies in high-altitude mountainous areas, comprising:

[0021] The first extraction module is used to extract the snow cover range in the water body data of the target mountainous area based on the true color synthesis and color feature extraction method of remote sensing data.

[0022] The second extraction module is used to extract the mountain shadow range from the water body data of the target mountain area based on the digital elevation model data;

[0023] The generation module is used to generate mask layers for snow cover and mountain shadow range by utilizing dilation and erosion operations in image morphology.

[0024] The removal module is used to remove the areas covered by the mask layer in the water body data of the target mountain area, so as to obtain the water body detection results of the target mountain area.

[0025] Optionally, the generation module is specifically used for:

[0026] Using dilation and erosion operations in image morphology, a mask layer for the snow cover range and the mountain shadow range is generated, including: detecting water noise in the target mountain water data other than the snow cover range and the mountain shadow range based on the neighborhood window detection method; removing the residual water noise to obtain the mask layer.

[0027] Optionally, the removal module is specifically used for:

[0028] The water noise remaining in the target mountain water data, excluding snow cover and mountain shadow areas, is divided into multiple windows of a preset size. Permanent water pixels are then identified in each window using a neighborhood detection method, and these permanent water pixels are marked to obtain the marked water data. If no permanent water pixels exist, the multiple windows are further divided into a second set of windows. Permanent water pixels are then identified in each of these second sets of windows using the same neighborhood detection method, and these permanent water pixels are marked to obtain the water detection results.

[0029] Optionally, the first extraction module is specifically used for:

[0030] Calculate the photometric component value of each pixel in the color space of the target mountain water body data;

[0031] Filter the pixels in the target mountain water body data whose photometric component value is greater than a preset threshold.

[0032] Calculate the frequency of pixels whose photometric component values ​​are greater than a preset photometric threshold and whose historical periods are classified as snow cover.

[0033] The range of snow cover is obtained by selecting pixels whose frequency of snow cover is greater than or equal to a preset probability threshold.

[0034] Optionally, the second extraction module is specifically used for:

[0035] The digital elevation model data is converted into slope to obtain multiple slope values;

[0036] Calculate the slope corresponding to multiple slope values ​​to obtain multiple slope values;

[0037] Filter out slope values ​​that are greater than or equal to a preset slope threshold from among multiple slope values;

[0038] Extract the mountain area containing the slope value that is greater than or equal to the preset slope threshold from multiple slope values ​​to obtain the mountain shadow range.

[0039] Optionally, the generation module is specifically used for:

[0040] The snow cover area and the mountain shadow area are subjected to expansion, erosion and closing operations to fill the gaps and interruptions in the snow cover area and the mountain shadow area, resulting in a mask layer.

[0041] Optionally, the device further includes:

[0042] The acquisition module is used by the first extraction module to integrate the remote sensing surface reflectance data of the target mountain area year by year before extracting the snow cover range in the target mountain water data based on the remote sensing data true color synthesis and color feature extraction method, so as to obtain the target mountain water data, which can be one or more.

[0043] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the method provided in the first aspect above are performed.

[0044] Fourthly, embodiments of this application provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0045] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. Attached Figure Description

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 A flowchart illustrating a method for detecting water bodies in high-altitude mountainous areas, provided as an embodiment of this application;

[0048] Figure 2 This is a statistical histogram of pixel brightness values ​​in a color composite image of a mountain provided in an embodiment of this application;

[0049] Figure 3 This application provides a statistical histogram of a certain mountain slope pixel.

[0050] Figure 4 A schematic diagram illustrating a method for determining a negative mask layer for a specific mountain terrain according to an embodiment of this application;

[0051] Figure 5A schematic block diagram of a device for detecting water bodies in high-altitude mountainous areas, provided as an embodiment of this application;

[0052] Figure 6 This is a schematic block diagram of a device for detecting water bodies in high-altitude and cold mountainous areas, provided as an embodiment of this application. Detailed Implementation

[0053] 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 a part of the embodiments of this application, and not all of them. 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. 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 represents selected embodiments of this 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.

[0054] 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.

[0055] This application is applied to water body detection scenarios, specifically, to further remove snow cover and mountain shadow areas from existing water body detection data to obtain accurate water body detection results.

[0056] Currently, the detection of water bodies in mountains can only be done directly using remote sensing data. This involves acquiring satellite images of the target mountain area and then identifying water areas within the images. However, this method only provides a rough estimate of water bodies within mountains and fails to address the common issues of mountain shadows and snow noise interference found in remote sensing data of surface water in high-altitude, cold regions worldwide.

[0057] Therefore, this application uses a method based on true-color synthesis and color feature extraction of remote sensing data to extract the snow cover range from the water body data of the target mountain area; and uses digital elevation model data to extract the mountain shadow range from the water body data of the target mountain area. Using dilation and erosion operations in image morphology, mask layers for the snow cover range and mountain shadow range are generated; and the areas covered by the mask layers in the water body data of the target mountain area are removed to obtain the water body detection results of the target mountain area. After extracting the snow cover range and mountain shadow range of the mountain, the algorithm can generate mask layers for the snow cover range and mountain shadow range. This allows for the removal of water bodies within the snow cover range and mountain shadow range from the existing water body data of the target mountain area, achieving accurate removal of noise interference from mountain shadows and snow cover during water body detection in high-altitude and cold mountain areas, thus improving the effectiveness of water body detection results.

[0058] In this embodiment of the application, the executing entity can be the high-altitude cold mountain water body detection equipment in the high-altitude cold mountain water body detection system. In actual applications, the high-altitude cold mountain water body detection equipment can be electronic devices such as terminal equipment and servers, and there are no restrictions here.

[0059] The following is combined Figure 1 The method for detecting water bodies in high-altitude and cold mountainous areas according to embodiments of this application will be described in detail.

[0060] Please refer to Figure 1 , Figure 1 A flowchart of a method for detecting water bodies in high-altitude mountainous areas provided in this application embodiment is shown below. Figure 1 The methods for detecting water bodies in high-altitude, cold mountainous areas shown include:

[0061] Step 110: Based on the true-color synthesis and color feature extraction method of remote sensing data, extract the snow cover range in the water body data of the target mountain area.

[0062] The water body data for the target mountain area can be the original mountain image data or existing water body data extracted from the original mountain image data. The mountain can be any mountain, including high-altitude and cold mountain areas. The snow cover range indicates the area in the target mountain area where snow is present.

[0063] In some embodiments of this application, before extracting the snow cover range from the water body data of the target mountain area based on the method of true color synthesis and color feature extraction of remote sensing data, the method further includes: integrating the remote sensing surface reflectance data of the target mountain area year by year to obtain the water body data of the target mountain area, wherein the water body data of the target mountain area is one or more.

[0064] In the above process, this application can filter out target mountain water body data for detection by using remote sensing surface reflectance data of the target mountain area over many years, so as to accurately detect the water body.

[0065] Among them, remote sensing surface reflectance data of the target mountain area are integrated year by year. For example, the Qinghai-Tibet Plateau, a typical high-altitude and cold mountain area, is selected as the study area. Taking 2021 as an example, remote sensing surface reflectance data of the Qinghai-Tibet Plateau from 1990 to 2022 are synthesized year by year based on Google Earth Engine (GEE).

[0066] In some embodiments of this application, the snow cover range in the target mountain water body data is extracted based on the remote sensing data true color synthesis and color feature extraction method. This includes: calculating the photometric component value of each pixel in the target mountain water body data in the color space; filtering pixels whose photometric component value is greater than a preset threshold; calculating the frequency of pixels whose photometric component value is greater than the preset photometric threshold being classified as snow cover in historical periods; and filtering pixels whose frequency of snow cover is greater than or equal to a preset probability threshold to obtain the snow cover range.

[0067] In the above process, this application can use the method of calculating the photometric component value to take the pixel corresponding to the photometric value range where the snow is located as the range where the snow is located, and accurately divide the snow range in the target mountain area image.

[0068] The luminance component value represents the brightness value of the image pixel in the target mountain area image. The preset threshold, preset luminance threshold, and preset probability threshold can be determined based on existing snow cover judgment conditions or set according to user needs.

[0069] Specifically, the photometric component value of each pixel in the color space of the target mountain water body data is obtained by the following formula: ;

[0070] Where L is the lightness component in the HLS color space, with a value between 0 and 255, representing the brightness of each pixel; Max is the maximum component value of a pixel in the RGB color space in the true color composite image, and Min is the minimum component value.

[0071] The frequency with which pixels whose photometric component values ​​are greater than a preset photometric threshold were classified as snow cover in historical periods is calculated using the following formula:

[0072] ;

[0073] Where F is the frequency with which a particular pixel is identified as a snow surface during a historical period; Count is the number of detections, i.e., the number of years; W y, iTo determine whether a pixel is classified as a snow surface, it is marked as 1 if it is, and 0 otherwise. Experiments have shown that the F-value of permanent snow surfaces is usually greater than or equal to 90%. Therefore, permanent snow surfaces are extracted using the following rule: if F ≥ 90%, it is a permanent snow surface pixel; otherwise, it is not. Based on the extraction of permanent snow surfaces, the snow cover area is obtained.

[0074] Step 120: Based on the digital elevation model data, extract the mountain shadow range from the water body data of the target mountain area.

[0075] The digital elevation model data can be obtained from existing data or filtered from this year's mountain data. The mountain shadow range represents the area covered by the shadow cast by the excessive height of the mountain.

[0076] In some embodiments of this application, the extraction of the mountain shadow range from the water body data of the target mountain area based on digital elevation model data includes: converting the digital elevation model data into slope to obtain multiple slope values; calculating the slope corresponding to the multiple slope values ​​to obtain multiple slope values; filtering the slope values ​​that are greater than or equal to a preset slope threshold from the multiple slope values; and extracting the mountain area where the slope values ​​that are greater than or equal to the preset slope threshold are located from the multiple slope values ​​to obtain the mountain shadow range.

[0077] In the above process, this application calculates the slope of each point in the target mountain area using digital elevation model data, and takes the mountain area with a slope greater than or equal to a preset slope threshold as the mountain shadow range, thus accurately dividing the mountain shadow range in the target mountain image.

[0078] The preset slope threshold and preset slope threshold can be determined by existing mountain shadow judgment conditions, or they can be set according to user needs.

[0079] Specifically: Converting digital elevation model data into slope values, resulting in multiple slope values, is achieved using the following formula: ; ; .

[0080] In this context, the elevation value at a specific location in the DEM data is represented as z(x, y), where (x, y) are the row and column coordinates of that point in the DEM raster data, and z is the elevation value of that coordinate point. The gradient in the DEM data is represented by a vector. <g x , g y > represents the rate of change of the DEM in each direction; where g x and g y represents the partial derivatives of the DEM along the x-axis and y-axis, respectively; M(x,y) represents the slope value of the DEM at that coordinate point.

[0081] The slope corresponding to multiple slope values ​​is calculated using the following formula: ;

[0082] Here, Slope is the slope at a given point (x, y), calculated using the arctangent function. The result of the arctangent function calculation is in radians, while slope is usually expressed in degrees; therefore, a conversion from radians to degrees is necessary.

[0083] Please refer to Figure 2 , Figure 2 Here is a statistical histogram of pixel brightness values ​​in a color composite image of a mountain provided in this application, such as... Figure 2 It can be seen that the number of pixels and the cumulative percentage of pixels corresponding to brightness values ​​between 250 and 255 are relatively large, and they are consistent with the brightness range of snow accumulation. Therefore, these pixels can be regarded as the snow accumulation range.

[0084] Step 130: Using dilation and erosion operations in image morphology, generate mask layers for the snow cover area and the mountain shadow area.

[0085] Among them, the mask layer represents the area covered by a certain color. For example, a gray area can be used to cover the snow cover area and the negative area of ​​the mountain in the target mountain water data to obtain a mask layer.

[0086] In some embodiments of this application, a mask layer for the snow cover area and the mountain shadow area is generated by using dilation and erosion operations in image morphology, including: performing dilation, erosion and closing operations on the snow cover area and the mountain shadow area to fill the gaps and interrupted areas of the snow cover area and the mountain shadow area to obtain the mask layer.

[0087] In the above process, this application can fill the gaps and interruptions in the undetected snow cover and mountain shadow areas through dilation, erosion and closing operations, and accurately obtain the mask layers corresponding to the snow cover and mountain shadow areas.

[0088] The method of filling gaps and breaks in the snow cover and mountain shadow areas represents the method of covering unreasonable breaks and gaps in the image with the color of a mask layer.

[0089] Specifically: Expansion, erosion, and closing operations are performed on the snow cover and mountain shadow areas to obtain gaps and breaks in these areas; filling these gaps and breaks in the snow cover and mountain shadow areas yields a mask layer obtained using the following formula:

[0090] ;

[0091] ;

[0092] ;

[0093] Wherein, the input image f(s, t) and its domain D f , Structural element b(x, y) and its domain D b The dilation operation enlarges the bright areas of the input image to enhance its edges. Simultaneously, dark pixels contained in the structuring element of the first formula are eliminated, reducing the number of remaining dark pixels. The erosion operation in the second formula is the opposite of dilation; the output image is darker relative to the input image. If the bright areas in the input image are smaller than the structuring element, the bright areas will be eliminated. The closing operation in the third formula is a combination of dilation and erosion in morphological image processing, generally used to remove small dark spots in the input image while preserving the original large bright features. Specifically, the closing operation first dilates the target object in the image, filling in small holes to achieve interruption and unification between adjacent objects. Then, it smooths the edges without significantly altering the target region to achieve the purpose of erosion.

[0094] In some embodiments, a mask layer for snow cover range and mountain shadow range is generated using dilation and erosion operations in image morphology, including: detecting water noise remaining in the target mountain water data in addition to snow cover range and mountain shadow range based on a neighborhood window detection method; removing the remaining water noise to obtain the mask layer.

[0095] In the above embodiments, this application can perform window processing on the mountain and water data to remove the water areas remaining in the mask layer, further eliminating water noise outside the snow cover area and the mountain shadow area, and obtaining a more accurate water area.

[0096] In some embodiments, removing the area covered by the mask layer from the target mountain water data to obtain the target mountain water detection result includes: dividing the water noise remaining in the target mountain water data (excluding snow cover and mountain shadow areas) into windows of a preset size to obtain multiple windows; using a neighborhood detection method to determine permanent water pixels in each of the multiple windows and marking the permanent water pixels to obtain marked water data; if no permanent water pixels exist, dividing the multiple windows into secondary windows to obtain a second set of multiple windows; using a neighborhood detection method to determine permanent water pixels in each of the second set of multiple windows and marking the permanent water pixels to obtain the water detection result.

[0097] In the above embodiments, this application can mark water pixels for each window individually, accurately excluding potential water pixels within the mountain shadow area and snow cover area.

[0098] The window size can be set according to requirements, for example, 60*60 or 30*30.

[0099] Please refer to Figure 3 , Figure 3 For example, the statistical histogram of a certain mountain slope pixel provided in this application. Figure 3 As can be seen, the percentage of surface water and soil area and mountain shadow area increases with the increase of slope value. As shown in the figure, the area with a slope of less than 10 can be regarded as the water body area.

[0100] Please refer to Figure 4 , Figure 4 A schematic diagram illustrating the method for determining a negative mask layer for a specific mountain terrain provided in this application, as shown below. Figure 4 (A) is the original composite image of a certain mountain; such as Figure 4 (B) is the result of identifying the potential shadow range of a certain mountain; such as Figure 4 (C) is the shadow outline generated by the closing operation on a certain mountain; such as Figure 4 (D) is a shadow mask layer for a certain mountain terrain.

[0101] Step 140: Remove the area covered by the mask layer in the target mountain water data to obtain the water detection results of the target mountain.

[0102] The water body detection results include images of the water body areas in the target mountainous region.

[0103] Specifically, after the masking layer processing described above, some fragmented snow surfaces or terrain shadows still exist in the spatial distribution map of surface water bodies in some valleys or hilly areas. Therefore, a neighborhood detection method is used to determine the potential water cover area. First, window detection is performed on each seasonal water body pixel. For example, it is determined whether there are permanent water body pixels within a 60 × 60 window centered on this pixel. If they exist, their pixel locations are marked; if not, the density of seasonal water body pixels within a 30 × 30 window centered on this pixel can be calculated. If the result is ≥0.3, the pixel location is also marked. Finally, the remaining unmarked, isolated, and scattered pixels are removed as noise pixels to obtain the water body detection results for the mountain area.

[0104] In the above Figure 1In the process described, this application uses a method of true-color synthesis and color feature extraction based on remote sensing data to extract the snow cover range from the water body data of the target mountain area; it uses digital elevation model data to extract the mountain shadow range from the water body data of the target mountain area; it uses dilation and erosion operations in image morphology to generate mask layers for the snow cover range and the mountain shadow range; and it removes the areas covered by the mask layers from the water body data of the target mountain area to obtain the water body detection results of the target mountain area. After extracting the snow cover range and the mountain shadow range of the mountain through the extraction method, the application of the algorithm can generate mask layers for the snow cover range and the mountain shadow range. This allows for the removal of water bodies within the snow cover range and the mountain shadow range based on the existing water body data of the target mountain area, achieving accurate removal of noise interference from mountain shadows and snow cover during water body detection in high-altitude and cold mountain areas, and improving the effectiveness of water body detection results.

[0105] Please refer to Table 1, which compares the accuracy of remote sensing detection of surface water bodies in high-altitude and cold regions before and after using this invention:

[0106] Table 1

[0107]

[0108] The results show that: (1) After removing mountain shadows and snow noise using the present invention, the accuracy of remote sensing detection of water bodies in the Qinghai-Tibet Plateau region is significantly improved from 65.9% (Kappa coefficient of 0.28) to 96.0% (Kappa coefficient of 0.86); (2) Compared with machine learning classification algorithms, the present invention can accurately detect mountain shadows and snow noise without a large number of samples and long training time, and then combine with conventional water extraction algorithms to achieve fine remote sensing detection of surface water bodies in high-altitude and cold regions; (3) The method proposed in this application has strong spatiotemporal mobility and is suitable for fine remote sensing detection of surface water bodies in major high-altitude and cold regions around the world over a long time series.

[0109] The previous text passed Figure 1 The methods for water body detection in high-altitude and cold mountainous areas are described below. Figures 5-6 A device for detecting water bodies in high-altitude, cold mountainous areas.

[0110] Please refer to Figure 5 This is a schematic block diagram of a device 500 for detecting water bodies in high-altitude mountainous areas provided in this embodiment of the application. The device 500 can be a module, program segment, or code on an electronic device. This device 500 is related to the above... Figure 1 The method implementation corresponds to this and can be executed. Figure 1 The various steps involved in the method embodiment, and the specific functions of the device 500, can be found in the description below. To avoid repetition, detailed descriptions are appropriately omitted here.

[0111] Optionally, the device 500 includes:

[0112] The first extraction module 510 is used to extract the snow cover range in the water body data of the target mountain area based on the true color synthesis and color feature extraction method of remote sensing data.

[0113] The second extraction module 520 is used to extract the mountain shadow range from the water body data of the target mountain area based on the digital elevation model data.

[0114] The generation module 530 is used to generate mask layers for the snow cover range and the mountain shadow range by utilizing dilation and erosion operations in image morphology.

[0115] The removal module 540 is used to remove the area covered by the mask layer in the water body data of the target mountain area to obtain the water body detection results of the target mountain area.

[0116] Optionally, the generation module is specifically used for:

[0117] Using dilation and erosion operations in image morphology, a mask layer for the snow cover range and the mountain shadow range is generated, including: detecting water noise in the target mountain water data other than the snow cover range and the mountain shadow range based on the neighborhood window detection method; removing the residual water noise to obtain the mask layer.

[0118] Optionally, the removal module is specifically used for:

[0119] Based on the neighborhood window detection method, this method detects water noise remaining in the target mountain water data, excluding snow cover and mountain shadow areas. The process includes: dividing the target mountain water data into windows of a preset size to obtain multiple windows; using the neighborhood detection method to identify permanent water pixels in each of the multiple windows and marking these permanent water pixels to obtain marked water data; if no permanent water pixels exist, the multiple windows are further divided into secondary windows to obtain a second set of windows; using the neighborhood detection method to identify permanent water pixels in each of the second set of windows and marking these permanent water pixels to obtain the water detection result.

[0120] Optionally, the first extraction module is specifically used for:

[0121] Calculate the photometric component value of each pixel in the target mountain water body data in the color space; filter pixels in the target mountain water body data whose photometric component value is greater than a preset threshold; calculate the frequency of pixels whose photometric component value is greater than the preset photometric threshold being classified as snow cover in historical periods; filter pixels whose frequency of snow cover is greater than or equal to a preset probability threshold to obtain the snow cover range.

[0122] Optionally, the second extraction module is specifically used for:

[0123] The digital elevation model data is converted into slopes to obtain multiple slope values; the slope corresponding to the multiple slope values ​​is calculated to obtain multiple slope values; the slope values ​​that are greater than or equal to a preset slope threshold are filtered out from the multiple slope values; the mountain area where the slope values ​​that are greater than or equal to the preset slope threshold are located is extracted from the multiple slope values ​​to obtain the mountain shadow range.

[0124] Optionally, the generation module is specifically used for:

[0125] The snow cover area and the mountain shadow area are subjected to expansion, erosion and closing operations to fill the gaps and interruptions in the snow cover area and the mountain shadow area, resulting in a mask layer.

[0126] Optionally, the device further includes:

[0127] The acquisition module is used by the first extraction module to integrate the remote sensing surface reflectance data of the target mountain area year by year before extracting the snow cover range in the target mountain water data based on the remote sensing data true color synthesis and color feature extraction method, so as to obtain the target mountain water data, which can be one or more.

[0128] Please refer to Figure 6 This is a schematic block diagram of a device for detecting water bodies in high-altitude mountainous areas, provided in an embodiment of this application. The device may include a memory 610 and a processor 620. Optionally, the device may further include a communication interface 630 and a communication bus 640. This device is similar to the one described above. Figure 1 The method implementation corresponds to this and can be executed. Figure 1 The specific functions of the device involved in the method embodiments can be found in the following description.

[0129] Specifically, memory 610 is used to store computer-readable instructions.

[0130] Processor 620 is used to process readable instructions stored in memory and is capable of executing... Figure 1 Each step in the method.

[0131] The communication interface 630 is used for signaling or data communication with other node devices. For example, it is used for communication with a server or terminal, or for communication with other device nodes, but the embodiments of this application are not limited thereto.

[0132] Communication bus 640 is used to enable direct communication between the above components.

[0133] In this embodiment, the communication interface 630 of the device is used for signaling or data communication with other node devices. The memory 610 can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Optionally, the memory 610 can also be at least one storage device located remotely from the aforementioned processor. The memory 610 stores computer-readable instructions, which, when executed by the processor 620, enable the electronic device to perform the aforementioned... Figure 1 The method process is shown. Processor 620 can be used on device 500 and is used to perform the functions in this application. Exemplarily, the processor 620 described above can be a general-purpose processor, 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, and the embodiments of this application are not limited thereto.

[0134] This application embodiment also provides a readable storage medium, wherein when the computer program is executed by a processor, it performs the following... Figure 1 The method process executed by the electronic device in the illustrated method embodiment.

[0135] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0136] In summary, this application provides a method, apparatus, device, and storage medium for detecting water bodies in high-altitude mountainous areas. The method includes: extracting the snow cover range from the target mountainous area's water body data based on true-color synthesis and color feature extraction methods using remote sensing data; extracting the mountain shadow range from the target mountainous area's water body data based on digital elevation model data; generating mask layers for the snow cover range and mountain shadow range using dilation and erosion operations in image morphology; and removing the area covered by the mask layers from the target mountainous area's water body data to obtain the water body detection result for the target mountainous area. This method can accurately remove noise interference from mountain shadows and snow cover during water body detection in high-altitude mountainous areas, improving the effectiveness of water body detection results.

[0137] In the several 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.

[0138] 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.

[0139] 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.

[0140] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of 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 scope of protection of this application. 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.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0142] 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.

Claims

1. A method for detecting water bodies in high-altitude, cold mountainous areas, characterized in that, include: Based on the method of true color synthesis and color feature extraction of remote sensing data, the snow cover range in the water body data of the target mountain area is extracted; The method based on true-color synthesis and color feature extraction of remote sensing data extracts the snow cover range from target mountain water body data, including: calculating the photometric component value of each pixel in the target mountain water body data in the color space; filtering pixels whose photometric component values ​​are greater than a preset threshold; calculating the frequency of pixels whose photometric component values ​​are greater than a preset threshold being classified as snow cover in historical periods; and filtering pixels whose frequency of snow cover is greater than or equal to a preset probability threshold to obtain the snow cover range. Based on digital elevation model (DEM) data, the shadow range of mountains in the target mountain water body data is extracted; the extraction of the shadow range of mountains in the target mountain water body data based on DEM data includes: converting the DEM data into slope to obtain multiple slope values; calculating the slope corresponding to the multiple slope values ​​to obtain multiple slope values; filtering the slope values ​​that are greater than or equal to a preset slope threshold from the multiple slope values; extracting the mountain area where the slope values ​​that are greater than or equal to the preset slope threshold are located from the multiple slope values ​​to obtain the shadow range of mountains; Using dilation and erosion operations in image morphology, mask layers are generated for the snow cover area and the mountain shadow area; The process of removing the area covered by the mask layer from the water body data of the target mountain area to obtain the water body detection result of the target mountain area includes: dividing the water noise remaining in the water body data of the target mountain area (excluding the snow cover area and the mountain shadow area) into multiple windows of a preset size; judging the permanent water body pixels in each of the multiple windows using a neighborhood detection method and marking the permanent water body pixels to obtain marked water body data; if there are no permanent water body pixels, dividing the multiple windows into a second set of multiple windows; judging the permanent water body pixels in each of the second set of multiple windows using the neighborhood detection method and marking the permanent water body pixels to obtain the water body detection result.

2. The method according to claim 1, characterized in that, The process of generating mask layers for the snow cover area and the mountain shadow area using dilation and erosion operations in image morphology includes: Based on the neighborhood window detection method, water noise remaining in the target mountain water data, excluding the snow cover area and the mountain shadow area, is detected. Remove the remaining water noise to obtain the mask layer.

3. The method according to any one of claims 1-2, characterized in that, The process of generating mask layers for the snow cover area and the mountain shadow area using dilation and erosion operations in image morphology includes: The snow cover area and the mountain shadow area are subjected to expansion, erosion and closing operations to fill the gaps and interruptions in the snow cover area and the mountain shadow area, thus obtaining the mask layer.

4. The method according to any one of claims 1-2, characterized in that, Before extracting the snow cover extent from the water body data of the target mountain area using the remote sensing data true-color synthesis and color feature extraction method, the method further includes: By integrating remote sensing surface reflectance data of the target mountain area year by year, water body data of the target mountain area is obtained, and the water body data of the target mountain area may be one or more.

5. A device for detecting water bodies in high-altitude, cold mountainous areas, characterized in that, include: The first extraction module is used to extract the snow cover range in the water body data of the target mountainous area based on the true color synthesis and color feature extraction method of remote sensing data. The first extraction module is specifically used to calculate the photometric component value of each pixel in the target mountain water body data in the color space; and to filter the pixels in the target mountain water body data whose photometric component value is greater than a preset threshold. Calculate the frequency of pixels whose photometric component values ​​are greater than a preset photometric threshold being classified as snow in historical periods; filter out pixels whose frequency of snow accumulation is greater than or equal to a preset probability threshold to obtain the snow accumulation range. The second extraction module is used to extract the mountain shadow range from the water body data of the target mountain area based on the digital elevation model data; The second extraction module is specifically used to convert the digital elevation model data into slope to obtain multiple slope values; calculate the slope corresponding to the multiple slope values ​​to obtain multiple slope values; filter the slope values ​​that are greater than or equal to a preset slope threshold among the multiple slope values; and extract the mountain area where the slope values ​​that are greater than or equal to the preset slope threshold are located among the multiple slope values ​​to obtain the mountain shadow range. The generation module is used to generate mask layers for the snow cover area and the mountain shadow area by using dilation and erosion operations in image morphology; The removal module is used to remove the area covered by the mask layer in the target mountain water data to obtain the water detection results of the target mountain. The removal module is specifically used to divide the water noise remaining in the target mountain water data, excluding the snow cover area and the mountain shadow area, into multiple windows of a preset size; to determine permanent water pixels in each of the multiple windows through a neighborhood detection method, and to mark the permanent water pixels to obtain marked water data; if no permanent water pixels exist, the multiple windows are divided into a second set of windows to obtain a second set of multiple windows; The permanent water body pixels are determined one by one in the second plurality of windows by the neighborhood detection method, and the permanent water body pixels are marked to obtain the water body detection result.

6. An electronic device, characterized in that, include: A memory and a processor, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, include: A computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1-4.

Images