Water surface contour line extraction method and device, processor and computer program product

Abstract

The embodiment of the invention provides a water surface contour line extraction method and device, a processor and a computer program product, and belongs to the technical field of remote sensing image and topographic map processing.The method comprises the steps that illumination simulation of a first angle and illumination simulation of a second angle are conducted on the basis of a digital elevation model corresponding to a target area, illumination intensity grid layers corresponding to illumination simulation of the first angle and the second angle are obtained; calculating an illuminance average value pixel by pixel for the illuminance grid layers corresponding to the illumination simulation of the first angle and the second angle, and obtaining an illuminance average value grid layer; determining a first grid layer according to grid pixels of which the illuminance average value is greater than or equal to a flat region segmentation threshold value in the illuminance average value grid layer; and performing vectorization processing and morphological optimization on the first grid layer to obtain a water surface contour line corresponding to the target area.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image and topographic map processing technology, specifically to a method, apparatus, processor, and computer program product for extracting water surface contour lines. Background Technology

[0002] In the process of topographic mapping, the extraction of water surface contours mainly relies on manual visual interpretation by operators to delineate them on digital orthophotos and digital elevation models. This traditional working method has the following drawbacks: low work efficiency; manual extraction of water features on 1:500 scale topographic maps typically requires an average of 5-6 person-days per km. 2 The extraction results are greatly affected by subjective factors, and different operators have different criteria for judging the boundaries of water bodies; it is difficult to respond in a timely manner in emergency mapping scenarios, such as when flood disaster assessment often requires the extraction of water body range within several hours.

[0003] Current mainstream automated water body extraction technologies are primarily designed for satellite remote sensing imagery, and their implementation can be divided into two main technical routes: the index method based on spectral features and the machine learning-based method. Specifically, while the index method, represented by the improved NDWI method proposed in patent CN120014456A, has the advantage of high computational efficiency, it has significant limitations in practical applications: First, this method is highly dependent on the spectral quality of the image. When processing aerial images containing only RGB bands, the lack of multispectral information leads to a decrease in extraction accuracy. Second, in complex scenes, it is difficult to effectively distinguish water bodies from objects with similar spectral features, such as shadows and dark asphalt pavements, especially in urban environments where the false detection rate is high. Furthermore, this method exhibits poor robustness to dynamic changes in the physical state of water bodies (such as seasonal freezing and thawing processes) and changes in the aquatic ecosystem (such as algal blooms caused by eutrophication and changes in suspended solids concentration). Because the spectral reflectance characteristics of water bodies vary significantly under different water quality conditions, a single index extraction method with a fixed threshold often struggles to adapt to these complex and variable spectral feature variations, leading to serious deviations in the extraction results. These inherent limitations restrict the effectiveness of this technology in aerial image processing and high-precision surveying.

[0004] Machine learning-based water extraction techniques, such as the spectral feature construction scheme proposed in patent CN114898208B, while achieving breakthroughs in theoretical accuracy, still face significant technical obstacles in practical engineering applications. First, in the data preparation stage, model training relies on tens of thousands of high-quality labeled samples, often requiring substantial manual labor. Furthermore, these methods demand high-end computer hardware, necessitating high-performance GPU servers and ample RAM. Second, the model's generalization performance is significantly limited. When applied to image data from different geographical regions (e.g., the water network of southern China versus arid regions of northern China) or different seasons (e.g., the wet season versus the dry season), extraction accuracy drops significantly, necessitating model retraining to adapt to the new environment. Third, when processing sub-meter high-resolution imagery, the model's insufficient ability to identify small water bodies and complex boundary features results in numerous fragmented polygons in the extraction results, severely impacting the topological quality and usability of the output. These limitations make existing machine learning methods insufficient to meet the demands of large-scale, high-precision operational production. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, processor, and computer program product for extracting water surface contours. This method can automatically identify the water surface contours of a target area with high accuracy and strong adaptability.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for extracting water surface contour lines, the method comprising:

[0007] Based on the digital elevation model corresponding to the target area, illumination simulations are performed at the first angle and the second angle respectively to obtain the illumination raster layers corresponding to the illumination simulations at the first angle and the second angle respectively.

[0008] For the illumination raster layers corresponding to the illumination simulations at the first and second angles, respectively, the average illumination value is calculated pixel by pixel to obtain the average illumination value raster layer.

[0009] The first raster layer is determined based on the raster cells in the illumination mean raster layer whose average illumination is greater than or equal to the flat region segmentation threshold; and

[0010] The first raster layer is vectorized and morphologically optimized to obtain the water surface contour line corresponding to the target area.

[0011] Preferably, the process of vectorizing and morphologically optimizing the first raster layer to obtain the water surface contour line corresponding to the target area includes:

[0012] The first raster layer is vectorized to obtain the corresponding first vector layer;

[0013] Based on the first vector layer and the digital orthophoto map corresponding to the target area, determine the first water area patch corresponding to the target area; and

[0014] Based on the first vector layer, the internal holes of the first water area patch are filled, and the boundary of the first water area patch is smoothed. The water surface contour line corresponding to the target area is obtained through feature-to-line processing.

[0015] Preferably, the method further includes: after obtaining the water surface contour line corresponding to the target area, refining the water surface contour line in the following manner:

[0016] Based on the first vector layer and the preset buffer width, the buffer zone range on both sides of the water surface outline is determined;

[0017] Based on the digital elevation model and the buffer range, obtain the slope raster layer within the buffer range;

[0018] A second raster layer is determined based on the raster pixels in the slope raster layer whose slope is less than or equal to the slope segmentation threshold.

[0019] The second raster layer is vectorized to obtain the corresponding second vector layer;

[0020] Based on the second vector layer and the digital orthophoto map corresponding to the target area, determine the second water feature corresponding to the target area; and

[0021] Based on the second vector layer, the internal holes of the second water area patch are filled, and the boundary of the second water area patch is smoothed. The refined water surface contour line corresponding to the target area is obtained through feature-to-line processing.

[0022] Preferably, the smoothing of the boundary of the first or second water area patch is implemented using a polynomial approximation algorithm based on an exponential kernel.

[0023] Preferably, the slope segmentation threshold is determined by analyzing the slope of the water area objects in the slope raster layer.

[0024] Preferably, obtaining the illumination raster layer corresponding to the illumination simulation at the first angle or the second angle includes:

[0025] The digital elevation model and the digital orthophoto map corresponding to the target area are stitched and cropped according to the boundary of the target area.

[0026] The digital elevation model is filled with depressions using a depression-filling tool to obtain the depression-filled digital elevation model; and

[0027] Using the mountain shadow tool, obtain the illumination raster layer corresponding to the illumination simulation at the first angle or the second angle of the digital elevation model after filling the depression.

[0028] Preferably, the first angle α and the second angle β satisfy the following relationship:

[0029] When α < 180°, β = α + 180°; and

[0030] When α>180°, β=α-180°.

[0031] Preferably, the flat area segmentation threshold is determined by analyzing the digital orthophoto map corresponding to the target area and the mean illumination raster layer.

[0032] A second aspect of the present invention provides a water surface contour extraction device, comprising: a memory configured to store instructions; and a processor configured to retrieve the instructions from the memory and to implement the water surface contour extraction method when executing the instructions.

[0033] A third aspect of the present invention provides a processor for running a program, wherein the program is executed to perform the water surface contour extraction method.

[0034] A fourth aspect of the present invention provides a computer program product, comprising a computer program that, when executed by a processor, implements the water surface contour extraction method.

[0035] This invention first simulates illumination from different angles based on the digital elevation model corresponding to the target area to obtain an average illumination raster layer. Then, it determines a first raster layer based on the raster pixels in the average illumination raster layer whose average illumination is greater than or equal to the flat area segmentation threshold. Finally, it performs vectorization and morphological optimization on the first raster layer to obtain the water surface outline corresponding to the target area. This method can automatically adapt to topographic map recognition tasks of different scales, reduce manual intervention, and meet the needs of automated mapping in complex terrain environments.

[0036] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0038] Figure 1 This is a schematic diagram illustrating the effects of light radiation on different terrains provided in the embodiments of the present invention;

[0039] Figure 2 This is a schematic flowchart of the water surface contour extraction method provided in an embodiment of the present invention;

[0040] Figure 3a This is a schematic diagram of a digital orthophoto containing a suspended object provided in an embodiment of the present invention;

[0041] Figure 3b This is a schematic diagram of a digital orthophoto image that does not contain suspended objects, provided in an embodiment of the present invention.

[0042] Figure 3c This is a schematic diagram of a digital elevation model containing suspended objects provided in an embodiment of the present invention;

[0043] Figure 3d This is a schematic diagram of a digital elevation model that does not contain suspended objects, provided in an embodiment of the present invention.

[0044] Figure 4a The embodiments of the present invention are based on Figure 3c The illumination raster layer obtained under a 135-degree lighting simulation;

[0045] Figure 4b The embodiments of the present invention are based on Figure 3d The illumination raster layer obtained under a 135-degree lighting simulation;

[0046] Figure 4c The embodiments of the present invention are based on Figure 3c The illumination raster layer obtained under a 315-degree lighting simulation;

[0047] Figure 4d The embodiments of the present invention are based on Figure 3d The illumination raster layer obtained under a 315-degree lighting simulation;

[0048] Figure 4e The embodiments of the present invention are based on Figure 4a and Figure 4c A schematic diagram of the obtained mean illumination raster layer;

[0049] Figure 4f The embodiments of the present invention are based on Figure 4b and Figure 4d A schematic diagram of the obtained mean illumination raster layer;

[0050] Figure 5a The embodiments of the present invention are based on Figure 4e A schematic diagram of the raster layer of the acquired flat area;

[0051] Figure 5b The embodiments of the present invention are based on Figure 4f A schematic diagram of the raster layer of the acquired flat area;

[0052] Figure 6a The embodiments of the present invention are based on Figure 5a Schematic diagram of the obtained flat area vector layer Figure 6b The embodiments of the present invention are based on Figure 6a A preliminary schematic diagram of the water surface outline;

[0053] Figure 7a The embodiments of the present invention are based on Figure 6b A schematic diagram of the obtained slope raster layer;

[0054] Figure 7b This is a schematic diagram of a slope grid layer that does not contain suspended objects, provided in an embodiment of the present invention.

[0055] Figure 8a The embodiments of the present invention are based on Figure 7a A detailed schematic diagram of the water surface contour extraction.

[0056] Figure 8b This is a schematic diagram of a finely extracted water surface contour line without suspended objects provided in an embodiment of the present invention;

[0057] Figure 9 This is a magnified schematic diagram of the vectorization of flat areas, the preliminary extraction of water surface contour lines, and the refined extraction of water surface contour lines provided in the embodiments of the present invention. Detailed Implementation

[0058] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0059] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0060] The inventors of this application discovered during their research that, based on the theory of optical radiation transfer, there is a significant difference in the reception of solar radiation between flat and sloping surfaces: flat areas can receive uniform radiation from all directions, while sloping areas, due to differences in orientation, result in an asymmetrical distribution of radiation intensity (i.e., high radiation intensity on sunny slopes and low radiation intensity on shady slopes). Figure 1 As shown, when subjected to radiation from two opposite directions, α and β, the water area and flat areas (C and D), due to their flat terrain, exhibit uniform distribution and no shadowing when receiving bidirectional radiation. However, in slope A, the area receives higher irradiance due to direct sunlight under β-direction radiation, while under α-direction radiation, a shadow area is formed, resulting in reduced irradiance. Conversely, slope B forms a shadow area under β-direction radiation and receives less radiation, while receiving more radiation under α-direction radiation. Moreover, the total bidirectional radiation in the slope area is always lower than that in the flat area.

[0061] Based on this, this invention proposes a method for extracting water surface contours, which can accurately and efficiently extract water surface contours of target areas. Figure 2 This is a flowchart illustrating the water surface contour extraction method provided in this embodiment of the invention. (Refer to...) Figure 2 The method provided in the embodiments of the present invention will be described in detail, such as... Figure 2 As shown, the method may include:

[0062] S101. Based on the digital elevation model corresponding to the target area, perform illumination simulations at the first angle and the second angle respectively to obtain the illumination raster layers corresponding to the illumination simulations at the first angle and the second angle respectively.

[0063] Specifically, a Digital Elevation Model (DEM) is a physical ground model that digitally simulates the terrain using limited terrain elevation data. It represents ground elevation using an ordered numerical array (X, Y, Z) and is a branch of Digital Terrain Modeling (DTM). DEMs are widely used in terrain analysis, engineering construction, disaster monitoring, and 3D modeling.

[0064] In some embodiments, obtaining the illumination raster layer corresponding to the illumination simulation of the first angle or the second angle in S101 includes:

[0065] S201. Based on the boundary of the target area, the digital elevation model and the digital orthophoto map corresponding to the target area are stitched and cropped.

[0066] Digital orthophotos (DOM) are image data generated by correcting projection differences, mosaicking, and cropping the original remote sensing images using a digital elevation model. They combine the geometric accuracy of maps with the realism of images.

[0067] In some embodiments, a high-precision digital elevation model (DEM) and digital orthophoto of the target area are acquired based on drone aerial photography, and then stitched and cropped according to the boundaries of the study area (e.g., the extracted range of reservoirs, lakes, ponds, etc.) to obtain a stitched and cropped digital elevation model (DEM). clip (Refer to Figure 3c , Figure 3d ) and digital orthophoto DOM_ clip (Refer to Figure 3a , Figure 3b ).

[0068] S202. Use a depression-filling tool to perform depression-filling processing on the digital elevation model to obtain the depression-filled digital elevation model;

[0069] Specifically, the depression-filling tool in hydrological analysis can be used to fill depressions in the digital elevation model (DEM) to fill in depressions smaller than a certain elevation difference z, thereby obtaining the filled DEM (Fill_DEM). _clip The z-value can be set as the basic contour interval, which is determined according to the map scale and terrain category, with reference to the GB / T 35650-2017 standard.

[0070] It should be noted that the z-value can also be set according to actual needs. For example, in some embodiments, it is necessary to draw a 1:500 topographic map, and the study area is a plain. Referring to the GB / T 35650-2017 standard, the first-order mean square error of the digital elevation model for flat land in a 1:500 topographic map is 0.20. Therefore, the z-value is set to 0.2m, that is, depressions with an elevation difference of less than 0.2m are filled to obtain the filled digital elevation model Fill_DEM. _clip .

[0071] S203. Using the mountain shadow tool, obtain the illumination raster layer corresponding to the illumination simulation at the first angle or the second angle of the digital elevation model after filling the depression.

[0072] Wherein, the second angle β is the azimuth angle opposite to the first angle α. That is, the first angle α and the second angle β satisfy the following relationship:

[0073] When α<180°, β=α+180°; and when α>180°, β=α-180°.

[0074] In some embodiments, S203 is specifically described with a first angle α = 135° and a second angle β = 315°.

[0075] First, using the hill shadow tool, the Fill_DEM digital elevation model after filling depressions was obtained based on S202. _clipSimulate illumination at a 135° azimuth angle, calculate the illuminance received at each location, and thus obtain the illuminance raster layer HillShade135 under the 135° azimuth illumination simulation (refer to...). Figure 4a , Figure 4b ).

[0076] Then, using the hill shadow tool, the Fill_DEM digital elevation model after filling the depressions was obtained based on S202. _clip Simulate illumination at an azimuth angle of 315°, calculate the illuminance received at each location, and thus obtain the illuminance raster layer HillShade315 under the azimuth angle illumination simulation (refer to...). Figure 4c , Figure 4d ).

[0077] S102. For the illumination grid layers corresponding to the illumination simulations of the first angle and the second angle, calculate the average illumination value pixel by pixel to obtain the average illumination value grid layer.

[0078] Specifically, the raster calculator in ArcGIS software can be used to calculate the average illumination value raster layer HillSdMean under two different directions, i.e., HillSdMean = (HillShade135 + HillShade315) / 2.

[0079] The final obtained average illumination raster layer is as follows: Figure 4e , Figure 4f As shown.

[0080] S103. Determine the first grid layer based on the grid cells in the average illumination value grid layer whose average illumination value is greater than or equal to the flat area segmentation threshold.

[0081] The flat region segmentation threshold is determined by analyzing the digital orthophoto map corresponding to the target region and the mean illumination raster layer.

[0082] In some embodiments, combined with digital orthophoto maps (DOM_) clip The HillSdMean illuminance mean raster layer was analyzed to determine the segmentation threshold 'a' between flat areas (e.g., water bodies, flat roads, farmland) and slopes or steep slopes. Through experimentation, the optimal value for 'a' was found to be 179.

[0083] Furthermore, using the raster calculator tool in ArcGIS, cells with values ​​less than 179 are set to empty. Entering `SetNull("hillshadeMean.tif"<179,1)` will yield the extraction result RS_Class1 for flat areas, as shown below. Figure 5a , Figure 5bAs shown.

[0084] S104. Perform vectorization and morphological optimization on the first raster layer to obtain the water surface outline corresponding to the target area.

[0085] Specifically, the vectorization and morphological optimization of the first raster layer described in S104 to obtain the water surface contour line corresponding to the target area includes:

[0086] S301. Perform vectorization processing on the first raster layer to obtain the corresponding first vector layer;

[0087] S302. Based on the first vector layer and the digital orthophoto map corresponding to the target area, determine the first water area patch corresponding to the target area;

[0088] Specifically, using the raster to vector tool, the RS_Class1 raster layer is vectorized to obtain the vectorized layer Shp_Class1 (e.g., ...). Figure 6a As shown), and combined with the digital orthophoto map DOM_ clip The first water area patch was selected.

[0089] S303. Based on the first vector layer, fill the internal hollow polygons of the first water area patch, smooth the boundary of the first water area patch, and obtain the water surface outline corresponding to the target area through feature-to-line processing.

[0090] Specifically, the "Eliminate Surface" tool in the ArcGIS Data Management Toolbox - Cartographic Generalization is used to fill the holes inside the first water area patch. Then, the "Smooth Surface" tool in Cartographic Generalization is used for boundary optimization (including smoothing), thus obtaining the vector surface range of the first water area patch. The "Feature to Line" tool is then used to transform the initially extracted first water surface patch into a shape as shown below. Figure 6b The water surface outline shown is Water_Coarse.

[0091] The smoothing process uses the PEAK algorithm—Polynomial Approximation with Exponential Kernel—which can calculate a smooth surface that does not pass through the inflection point of the input surface, with a smoothing tolerance of 2.5 meters.

[0092] In some embodiments, the method further includes: after obtaining the water surface contour line corresponding to the target area, refining the water surface contour line in the following manner:

[0093] S401. Determine the buffer zone range on both sides of the water surface outline based on the first vector layer and the preset buffer width;

[0094] Specifically, the buffer analysis tool can be used to set a buffer of m meters on both sides of the water surface contour line Water_Coarse extracted by S104. The m value can be set with reference to the slope width of the actual extracted water area object to ensure that the position of the actual water surface contour line is within the buffer range, preferably near the center line.

[0095] In some embodiments, the m value is preferably 5 meters (10 meters in total on both sides). The side type is set to FULL using the buffer analysis tool, that is, for line input features, buffers will be generated on both sides of the line. The end type is set to ROUND, and the end of the buffer is a semi-circle.

[0096] S402. Based on the digital elevation model and the buffer range, obtain the slope raster layer within the buffer range;

[0097] Specifically, the slope layer can be calculated using the slope tool in the surface analysis toolbox, and the slope raster layer within the buffer area can be extracted using the mask extraction tool (or the clipping tool). Figure 7a , Figure 7b (As shown).

[0098] S403. Determine the second grid layer based on the grid cells in the slope grid layer whose slope is less than or equal to the slope segmentation threshold;

[0099] The slope segmentation threshold is determined by analyzing the slope of the water area objects in the slope raster layer.

[0100] Specifically, the slope of the water body object is analyzed, and the slope segmentation threshold b = 15° is set. That is, raster cells with slope ≤ 15° are extracted. Using the raster calculator, cells with slope > 15° are set to null values, and cells with slope ≤ 15° are set to 1. That is, input SetNull(Slope>15,1) to obtain the RsSlope_Class layer.

[0101] S404. Perform vectorization processing on the second raster layer to obtain the corresponding second vector layer;

[0102] Specifically, use the Raster to Face tool to convert the RsSlope_Class layer into a vector layer ShpSlope_Class, and select Simplify Faces, which will smooth the faces into simple shapes.

[0103] S405. Based on the second vector layer and the digital orthophoto map corresponding to the target area, determine the second water area patch corresponding to the target area;

[0104] S406. Based on the second vector layer, fill the internal holes of the second water area patch, smooth the boundary of the second water area patch, and then use the feature to line tool to obtain the refined water surface outline corresponding to the target area.

[0105] In some embodiments, the smoothing of the boundary of the first or second water area patch is implemented using a polynomial approximation algorithm based on an exponential kernel.

[0106] In some embodiments, the area removal tool is used to fill the voids inside the second water area patch. In this case, the area threshold can be set very large, larger than the area of ​​the inner circle. For example, in some embodiments, the area threshold is set to 100,000 square meters. Selecting "Remove only portions completely contained within other parts" fills voids with an internal area less than 100,000 square meters. Then, the boundary line is optimized using the smooth surface tool, and after feature-to-line conversion, the result is as follows: Figure 8a , Figure 8b , Figure 9 The refined water surface outline shown is Water_Fine.

[0107] In some embodiments, after obtaining the finely extracted water surface contour line Water_Fine, the system determines whether there are suspended objects in the water area by comparing the digital orthophoto map corresponding to the target area. If there are suspended objects in the water area, the system uses an editor tool to reshape the suspended objects.

[0108] It should also be noted that when implementing the water surface contour extraction method provided by this invention, ArcGIS software can be used to process and analyze the data, but it is not limited to this software. Other GIS analysis platforms can also be used for data calculation and analysis.

[0109] This invention provides a water surface contour extraction device, comprising: a memory configured to store instructions; and a processor configured to retrieve the instructions from the memory and to implement the water surface contour extraction method when executing the instructions.

[0110] This invention provides a processor for running a program, wherein the program is executed to perform the water surface contour extraction method.

[0111] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the water surface contour extraction method.

[0112] The water surface contour extraction method provided by this invention has significant advantages in terms of accuracy, efficiency, and applicability compared to traditional methods such as manual drawing and machine learning algorithms. This method is particularly suitable for surveying and mapping tasks with high timeliness requirements, wide coverage, and stringent accuracy requirements. It can meet multi-level business needs from daily monitoring to emergency response. For example, in emergency surveying and mapping, it can significantly improve the response speed of flood disaster assessment and geological disaster monitoring; in the field of basic surveying and mapping, it provides efficient technical support for large-scale topographic map updates and the construction of a real-scene 3D China; and in the water conservancy industry, it enables rapid monitoring of river and lake shorelines and intelligent upgrading of water conservancy project planning and design.

[0113] The water surface contour extraction method provided by this invention also has the following beneficial effects:

[0114] ① High efficiency and low power consumption, strong hardware compatibility: The water surface contour extraction method provided by this invention adopts a lightweight algorithm design, which reduces computing resource consumption by more than 60% compared with artificial intelligence image recognition algorithms. It can run smoothly on low-configuration computers, is suitable for wide range of data processing, can meet real-time processing requirements, and significantly improves work efficiency.

[0115] ② Highly adaptive and highly automated: The water surface contour extraction method provided by this invention does not require sample training. It adopts adaptive feature extraction technology, which can automatically adapt to topographic map recognition tasks of different scales, reduce manual intervention, and meet the needs of automated mapping in complex terrain environments.

[0116] ③ High accuracy, minimal manual correction, and significantly improved efficiency: The water surface contour extraction method provided by this invention achieves an initial accuracy of over 85%. Combined with intelligent morphological optimization processing, it further reduces the amount of manual correction required. Compared with traditional manual mapping, the overall efficiency is improved by 80%, significantly reducing operating costs and making it suitable for high-precision topographic mapping tasks.

[0117] ④ Technological innovation, interdisciplinary integration, and high promotional value: This invention innovatively incorporates the principles of solar radiation into topographic mapping, combined with a lightweight algorithm design, to improve robustness while ensuring high accuracy. The algorithm has a simple structure, is easy to expand, and can be widely applied in fields such as natural resource surveys, water conservancy projects, and geological disaster monitoring, possessing significant engineering application value and market promotion potential.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0123] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0124] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0125] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0126] The above are merely embodiments of this application and are not intended to limit the scope 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 the claims of this application.

Claims

1. A method for extracting water surface contours, characterized in that, The method includes: Based on the digital elevation model corresponding to the target area, illumination simulations are performed at the first angle and the second angle respectively to obtain the illumination raster layers corresponding to the illumination simulations at the first angle and the second angle respectively. For the illumination raster layers corresponding to the illumination simulations at the first and second angles, respectively, the average illumination value is calculated pixel by pixel to obtain the average illumination value raster layer. The first raster layer is determined based on the raster cells in the illumination mean raster layer whose average illumination is greater than or equal to the flat region segmentation threshold; and The first raster layer is vectorized and morphologically optimized to obtain the water surface contour line corresponding to the target area.

2. The method according to claim 1, characterized in that, The process of vectorizing and morphologically optimizing the first raster layer to obtain the water surface contour line corresponding to the target area includes: The first raster layer is vectorized to obtain the corresponding first vector layer; Based on the first vector layer and the digital orthophoto map corresponding to the target area, determine the first water area patch corresponding to the target area; and Based on the first vector layer, the internal holes of the first water area patch are filled, and the boundary of the first water area patch is smoothed. The water surface contour line corresponding to the target area is obtained through feature-to-line processing.

3. The method according to claim 2, characterized in that, The method further includes: after obtaining the water surface contour line corresponding to the target area, refining the water surface contour line in the following way: Based on the first vector layer and the preset buffer width, the buffer zone range on both sides of the water surface outline is determined; Based on the digital elevation model and the buffer range, obtain the slope raster layer within the buffer range; A second raster layer is determined based on the raster pixels in the slope raster layer whose slope is less than or equal to the slope segmentation threshold. The second raster layer is vectorized to obtain the corresponding second vector layer; Based on the second vector layer and the digital orthophoto map corresponding to the target area, determine the second water feature corresponding to the target area; and Based on the second vector layer, the internal holes of the second water area patch are filled, and the boundary of the second water area patch is smoothed. The refined water surface contour line corresponding to the target area is obtained through feature-to-line processing.

4. The method according to claim 3, characterized in that, The smoothing of the boundary of the second water area patch is achieved using a polynomial approximation algorithm based on an exponential kernel.

5. The method according to claim 3, characterized in that, The slope segmentation threshold is determined by analyzing the slope of the water area objects in the slope raster layer.

6. The method according to claim 1, characterized in that, Obtaining the illumination raster layer corresponding to the illumination simulation at the first angle or the second angle includes: The digital elevation model and the digital orthophoto map corresponding to the target area are stitched and cropped according to the boundary of the target area. The digital elevation model is filled with depressions using a depression-filling tool to obtain the depression-filled digital elevation model; and Using the mountain shadow tool, obtain the illumination raster layer corresponding to the illumination simulation at the first angle or the second angle of the digital elevation model after filling the depression.

7. The method according to claim 1, characterized in that, The first angle α and the second angle β satisfy the following relationship: When α < 180°, β = α + 180°; and When α>180°, β=α-180°.

8. The method according to claim 1, characterized in that, The flat region segmentation threshold is determined by analyzing the digital orthophoto map corresponding to the target region and the mean illumination raster layer.

9. A device for extracting water surface contours, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the water surface contour extraction method according to any one of claims 1 to 8.

10. A processor, characterized in that, Used to run a program, wherein the program is run to perform: the water surface contour extraction method as described in any one of claims 1 to 8.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the water surface contour extraction method as described in any one of claims 1 to 8.

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