Water body extraction method, device, equipment and medium

Abstract

The present invention provides a method, apparatus, device and medium for water body extraction, which relates to the technical field of image processing, and includes: estimating and screening the baselines between any two satellites in a multi-satellite formation according to single-look complex data to obtain multiple groups of target baselines, and extracting a first initial water body area in a target area based on the multiple groups of target baselines; extracting the initial coherence coefficients between any two satellites in the multi-satellite formation according to the single-look complex data, fusing the multiple initial coherence coefficients by using the multiple groups of target baselines to obtain a target coherence coefficient, and extracting a second initial water body area in the target area based on the target coherence coefficient; fusing the first initial water body area and the second initial water body area to extract the target water body area in the target area. The present invention can effectively reduce the situation of misidentifying the water body area and significantly improve the accuracy of the water body extraction result.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular, to a method, device, equipment and medium for water body extraction. Background Technique

[0002] Synthetic Aperture Radar (SAR), as an active remote sensing technology, is widely used in the field of water body extraction. SAR can obtain surface information all day and all weather, and is insensitive to cloud cover, which gives it unique advantages in water body extraction during flood disasters. Currently, the water body extraction technology based on SAR mainly utilizes the significant difference between water bodies and other ground objects in radar echo signals, especially the low backscattering characteristics caused by the smoothness of the water surface. For example, the water body extraction technology based on the SAR coherence threshold method.

[0003] Among them, the water body extraction technology based on the SAR coherence threshold method utilizes the coherence difference between water bodies and land in SAR images to identify water body areas. SAR coherence refers to the degree of consistency of signals in two SAR images obtained for the same ground object, usually obtained through interferometric measurement technology. Due to the smooth and dynamic nature of the water surface, it shows low coherence in the SAR images of the two images, while the land area usually shows high coherence. Therefore, the threshold segmentation method based on coherence can effectively distinguish water body and land areas. However, in actual water body extraction operations, in complex areas such as mountains or cities, there are interferences from shadows and buildings on water body extraction, resulting in low accuracy of water body extraction results. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for water body extraction, which can effectively reduce the situation of misidentifying water body areas and significantly improve the accuracy of water body extraction results.

[0005] In the first aspect, the present invention provides a method for water body extraction, including:

[0006] Obtain the single-look complex data collected by each satellite in the multi-satellite formation for the target area;

[0007] According to the single-look complex data, estimate and screen the baselines between any two satellites in the multi-satellite formation to obtain multiple groups of target baselines, and extract the first initial water body area in the target area based on the multiple groups of target baselines;

[0008] According to the single-look complex data, extract the initial coherence coefficients between any two satellites in the multi-satellite formation, fuse the multiple initial coherence coefficients using the multiple groups of target baselines to obtain the target coherence coefficient, and extract the second initial water body area in the target area based on the target coherence coefficient;

[0009] Fuse the first initial water body area and the second initial water body area to extract the target water body area within the target area.

[0010] In one implementation, based on single-look complex data, estimate and screen the baselines between any two satellites in the multi-satellite formation to obtain multiple groups of target baselines, including:

[0011] For any two satellites in the multi-satellite formation, based on the orbital parameters of the two satellites when each collects single-look complex data, estimate a group of baselines between the two satellites and their corresponding elevation ambiguities;

[0012] Compare the elevation ambiguities corresponding to each group of baselines, and screen out multiple groups of target baselines from each group of baselines according to the comparison results.

[0013] In one implementation, extract the first initial water body area within the target area based on multiple groups of target baselines, including:

[0014] For each group of target baselines, generate an interferogram corresponding to the target baseline based on the single-look complex data collected by the two satellites to which the target baseline belongs;

[0015] Perform phase unwrapping processing on the interferogram corresponding to each group of target baselines through a multi-baseline phase unwrapping model set to obtain the target unwrapped phase;

[0016] Convert the target unwrapped phase into the target surface elevation corresponding to the target area;

[0017] Extract the first initial water body area from the target area according to the target surface elevation.

[0018] In one implementation, perform phase unwrapping processing on the interferogram corresponding to each group of target baselines through a multi-baseline phase unwrapping model set to obtain the target unwrapped phase, including:

[0019] Perform phase unwrapping processing on the interferogram corresponding to each group of target baselines through each multi-baseline phase unwrapping model in the multi-baseline phase unwrapping model set to obtain the initial unwrapped phase output by each multi-baseline phase unwrapping model;

[0020] Determine the difference between each initial unwrapped phase and a preset unwrapped phase threshold respectively; wherein, the preset unwrapped phase threshold is obtained by back-calculating using the known surface elevation corresponding to the target area;

[0021] Determine the initial unwrapped phase corresponding to the smallest difference as the target unwrapped phase.

[0022] In one implementation, extract the first initial water body area from the target area according to the target surface elevation, including:

[0023] Determine the slope value of each pixel in the target area using the target ground surface elevation;

[0024] Determine whether the slope value of the pixel is within a preset slope range;

[0025] If so, determine that the pixel belongs to a suspected water body pixel; if not, determine that the pixel does not belong to a suspected water body pixel, so as to extract the first initial water body area in the target area.

[0026] In one implementation, fusing multiple initial coherence coefficients using multiple groups of target baselines to obtain a target coherence coefficient, including:

[0027] From multiple initial coherence coefficients, screen out the initial coherence coefficients between two satellites to which the multiple groups of target baselines belong;

[0028] Determine the fusion weight based on the elevation ambiguity corresponding to multiple groups of target baselines;

[0029] Use the fusion weight to fuse the screened initial coherence coefficients to obtain a target coherence coefficient.

[0030] In one implementation, extracting a second initial water body area in the target area based on the target coherence coefficient, including:

[0031] Determine whether the target coherence coefficient of each pixel in the target area is lower than a preset coherence threshold;

[0032] If so, determine that the pixel belongs to a suspected water body pixel; if not, determine that the pixel does not belong to a suspected water body pixel, so as to extract the second initial water body area in the target area.

[0033] In a second aspect, the present invention further provides a water body extraction device, including:

[0034] A data acquisition module, configured to acquire single-look complex data collected by each satellite in the multi-satellite formation for the target area;

[0035] A first water body extraction module, configured to estimate and screen the baselines between any two satellites in the multi-satellite formation according to the single-look complex data to obtain multiple groups of target baselines, and extract the first initial water body area in the target area based on the multiple groups of target baselines;

[0036] A second water body extraction module, configured to extract the initial coherence coefficients between any two satellites in the multi-satellite formation according to the single-look complex data, fuse multiple initial coherence coefficients using multiple groups of target baselines to obtain a target coherence coefficient, and extract the second initial water body area in the target area based on the target coherence coefficient;

[0037] A target water body extraction module, configured to fuse the first initial water body area and the second initial water body area to extract the target water body area within the target area.

[0038] In a third aspect, the present invention further provides an electronic device, including a processor and a memory. The memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method according to any one of the first aspect.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the method according to any one of the first aspect.

[0040] A water body extraction method, device, equipment and medium provided by the present invention. First, obtain the single-look complex data collected by each satellite in the multi-satellite formation for the target area; then, according to the single-look complex data, estimate and screen the baselines between any two satellites in the multi-satellite formation to obtain multiple groups of target baselines, and extract the first initial water body area within the target area based on the multiple groups of target baselines; at the same time, according to the single-look complex data, extract the initial coherence coefficients between any two satellites in the multi-satellite formation, fuse the multiple initial coherence coefficients using the multiple groups of target baselines to obtain the target coherence coefficient, and extract the second initial water body area within the target area based on the target coherence coefficient; finally, fuse the first initial water body area and the second initial water body area to extract the target water body area within the target area. The above method extracts the first initial water body area within the target area based on multiple groups of target baselines; at the same time, fuses the initial coherence coefficients between any two satellites in the multi-satellite formation using the multiple groups of target baselines, and extracts the second initial water body area within the target area based on this; fuses the two initial water body areas to determine the target water body area within the target area. The present invention combines baseline information and coherence information, making up for the problem of misidentifying flat non-water areas by only using baseline information and misidentifying layover and shadow areas by only using coherence information, thus significantly improving the accuracy of the water body extraction result.

[0041] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0042] To make the above objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0043] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a schematic flow chart of a water body extraction method provided by an embodiment of the present invention;

[0045] Figure 2 It is a schematic overall flow chart of a water body extraction method provided by an embodiment of the present invention;

[0046] Figure 3 It is a schematic structural diagram of a water body extraction device provided by an embodiment of the present invention;

[0047] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Specific Embodiments

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] Currently, in the water body extraction technology based on the SAR coherence threshold method, there are interferences from shadows and buildings on water body extraction, and it is also necessary to combine terrain data or other auxiliary information to optimize the water body extraction results. Therefore, to obtain the rapid and accurate water body range during flood disasters and reduce the errors introduced by decorrelation caused by time changes. At the same time, with the successful launch of the "Cartwheel" formation satellites represented by the "Space Macro Figure 1 No." satellite, terrain and image data can be obtained simultaneously in one shot. A more real-time, more time-sensitive, higher-accuracy water body extraction method applicable to the "Cartwheel formation" satellite data has become a necessity.

[0050] Based on this, the embodiments of the present invention provide a water body extraction method, device, equipment, and medium, which can effectively reduce the situation of misidentifying water body areas and significantly improve the accuracy of water body extraction results.

[0051] For the convenience of understanding this embodiment, first, a water body extraction method disclosed in the embodiments of the present invention will be introduced in detail. SeeFigure 1 Schematic flow chart of a water body extraction method shown, the method mainly includes the following steps S102 to step S108:

[0052] Step S102, obtain single-look complex data collected by each satellite in the multi-satellite formation for the target area.

[0053] Among them, the multi-satellite formation includes a main satellite and multiple auxiliary satellites, such as a "wheel-type" formation, which includes a main satellite and three auxiliary satellites. Single-look complex data (Single Look Complex, abbreviated as SLC) is the result of the original data collected by the satellite after being processed by the pulse compression algorithm and the SAR synthesis algorithm. The single-look complex data includes at least SAR image data. In one example, after obtaining the single-look complex data collected by each satellite for the target area, the single-look complex data can be preprocessed, and the preprocessing includes import processing, registration processing, denoising processing, etc.

[0054] Step S104, according to the single-look complex data, estimate and screen the baseline between any two satellites in the multi-satellite formation to obtain multiple groups of target baselines, and extract the first initial water body area in the target area based on the multiple groups of target baselines.

[0055] Among them, the category of the pixels in the first initial water body area is suspected water body. In one implementation, first estimate the baseline between any two satellites in the multi-satellite formation; then use the elevation ambiguity to screen the multiple groups of baselines obtained by estimation to obtain multiple groups of target baselines, which can achieve a better unwrapping effect in the subsequent multi-baseline phase unwrapping process; finally, based on the multiple groups of target baselines, perform processing such as generating a de-flattened interferogram, multi-baseline phase unwrapping, surface elevation extraction, water body area extraction (also known as terrain information extraction), etc., to extract the first initial water body area in the target area.

[0056] Step S106, according to the single-look complex data, extract the initial coherence coefficient between any two satellites in the multi-satellite formation, fuse multiple initial coherence coefficients using multiple groups of target baselines to obtain the target coherence coefficient, and extract the second initial water body area in the target area based on the target coherence coefficient.

[0057] Among them, the category of the pixels in the second initial water body area is suspected water body. In one implementation, after extracting the initial coherence coefficient between any two satellites in the multi-satellite formation, multiple corresponding initial coherence coefficients can be screened out according to multiple groups of target baselines, and the fusion weights of the multiple initial coherence coefficients can be determined using the elevation ambiguity corresponding to the multiple groups of target baselines to achieve the fusion of the multiple initial coherence coefficients, and then the second initial water body area in the target area can be extracted using the fused target coherence coefficient.

[0058] Step S108: Fuse the first initial water body area and the second initial water body area to extract the target water body area within the target area.

[0059] Among them, the category of the pixels within the target water body area is water body. In one implementation, the first initial water body area and the second initial water body area can be intersected, and the intersection of the two initial water body areas is used as the target water body area.

[0060] The water body extraction method provided by the embodiments of the present invention extracts the first initial water body area within the target area based on multiple sets of target baselines; at the same time, fuses the initial coherence coefficients between any two satellites within the multi-satellite formation using multiple sets of target baselines, and on this basis, extracts the second initial water body area within the target area; fuses the two initial water body areas to determine the target water body area within the target area. By combining baseline information and coherence information, the present invention makes up for the misidentification of flat non-water body areas when only using baseline information and the misidentification of layover and shadow areas when only using coherence information, thus significantly improving the accuracy of the water body extraction result.

[0061] In a specific implementation, taking the multi-satellite formation of the "Cartwheel" formation as an example, the embodiments of the present invention propose a high-precision water body extraction method based on real-time terrain production and not affected by temporal decorrelation according to the characteristics of the "Cartwheel" formation.

[0062] The "Cartwheel" formation configuration is a specific satellite formation design, in which four synthetic aperture radar satellites orbit the Earth in a specific orbit and present a layout similar to that of a wheel. Among these satellites, one is usually located at the center of the formation as the main satellite, and the other three satellites fly in orbits with different eccentricities, forming a four-star structure orbiting the main satellite at any moment. This formation can improve the monitoring accuracy of ground targets through multi-angle observations, and is particularly suitable for tasks such as interferometric synthetic aperture radar (InSAR) and high-resolution imaging. Taking the world's first four-star "Cartwheel" formation satellite constellation "Space Macro Figure 1 No." as an example, Satellite A of "Space Macro Figure 1 No." is the main satellite, and Satellites B, C, and D are auxiliary satellites. One shot can obtain four images of the same area. After being paired in pairs, 6 groups of baselines with different lengths can be formed, and high-precision terrain data can be produced in real time for various complex terrains. At the same time, the coherence between water bodies and the land surface is significantly different. The coherence of the land surface photographed at the same moment is extremely high, while the coherence of water bodies is relatively low, and water bodies can be extracted more accurately and effectively.

[0063] An embodiment of the present invention designs a water body extraction method based on the image data of a "wheel-type" satellite formation by combining the advantages of the "wheel-type" satellite formation. Refer to Figure 2 The overall process schematic diagram of a water body extraction method shown in

[0064] (1) Data preprocessing: The preprocessing mainly includes the import, registration, and denoising of the main and auxiliary satellites.

[0065] Since the payload storage formats of different spaceborne SAR satellites are different, their single-look complex (SLC) data formats are also different. For the sake of unified processing, it is necessary to perform input import operations on the SLC data of each satellite. By reading data files and metadata in different formats, they are converted into data and xml metadata files in a unified format.

[0066] Image registration is processed according to the strategy of hierarchical registration of coarse registration and fine registration. Coarse registration calculates the initial offset of the main and auxiliary images through the orbital information in the metadata files of the main and auxiliary satellites, using the imaging Doppler equation, range equation, and reference ellipsoid equation. All three auxiliary satellites are registered with the main satellite as the reference, and the registration accuracy can reach ±5 pixels in the range direction and ±10 pixels in the azimuth direction.

[0067] Coarse registration performs azimuth correction through the imaging Doppler equation. By analyzing the Doppler frequency information, the error in the azimuth direction can be corrected. The relationship between the Doppler frequency shift, the target position, the radar flight speed, and the relative motion speed between the radar and the target can be expressed by the formula:

[0068]

[0069] where f D is the Doppler frequency, v r is the radial velocity of the target relative to the radar, and λ is the wavelength of the radar.

[0070] The correction in the range direction is achieved by calculating the slant range through the range equation. The range equation calculates the distance between the radar and the ground target through the time delay of the pulse signal emitted by the radar reaching the target and then returning. The specific formula is as follows:

[0071]

[0072] where R is the distance from the target to the radar, c is the speed of light, and Δt is the round-trip time of the signal.

[0073] Finally, it is also necessary to ensure the consistency of the image in the geographic space through the reference ellipsoid equation and project the image onto a unified geographic coordinate system.

[0074] Fine registration is sub-pixel level registration based on coarse registration. The most commonly used method is the cross-correlation method. By collecting multiple local windows on the primary and secondary images and calculating the cross-correlation coefficients of the pixel values in each window, the subtle displacement between the images can be accurately estimated to achieve fine registration at the sub-pixel level.

[0075] (2) Baseline optimization, including:

[0076] (2.1) For any two satellites within a multi-satellite formation, based on the orbital parameters when the two satellites collect single-look complex data respectively, a set of baselines between the two satellites and their corresponding elevation ambiguities are estimated.

[0077] Among them, the baseline estimation process is as follows: Baseline estimation depends on the orbital parameters in the xml metadata file. By reading the precise orbital information and according to the spatial geometric relationship, combined with the satellite orbital state information, the components of the baseline are calculated. The spatial baseline can usually be regarded as a three-dimensional vector between the positions of the radar satellites during two imaging processes. In interferometric applications, the baseline vector is decomposed into a vertical baseline and a horizontal baseline, and their impacts on the interferometric phase and terrain error are different. The vertical baseline B N and the horizontal baseline B T The formulas are:

[0078] B T = R 1 cos(θ 1 ) - R 2 cos(θ 2 );

[0079] B N = R 1 sin(θ 1 ) - R 2 sin(θ 2 );

[0080] Among them, R 1 and R 2 are the slant ranges of the two imaging processes respectively, and θ 1 and θ 2 are the incident angles of the two imaging processes respectively.

[0081] Exemplarily, according to the above formulas, the vertical baseline B N and the horizontal baseline B T between the primary satellite A and the secondary satellite B, the vertical baseline B N and the horizontal baseline B T between the primary satellite A and the secondary satellite C, the vertical baseline B N and the horizontal baseline B T between the primary satellite A and the secondary satellite D, and the vertical baseline B Nand the horizontal baseline B T the vertical baseline B between the secondary satellite B and the secondary satellite D N and the horizontal baseline B T the vertical baseline B between the secondary satellite C and the secondary satellite D N and the horizontal baseline B T , a total of 6 groups of baselines.

[0082] The calculation process of the elevation ambiguity is as follows: Since the length of the vertical baseline determines the accuracy of the generated elevation, the length distribution of the vertical baseline should be selected to be uniform, and the elevation ambiguity should be selected for the interferometric pairs suitable for multi-baseline phase unwrapping. This is because the uniformly distributed baseline lengths help to capture the detailed changes of the terrain at different scales, while reducing the ambiguity and uncertainty of certain specific length baselines during the unwrapping process. Through the data after baseline optimization, better unwrapping effects can be achieved in various multi-baseline phase unwrapping algorithms. The elevation ambiguity calculation formula is as follows:

[0083]

[0084] where h amb is the elevation ambiguity, that is, the elevation change corresponding to one full cycle of phase change, λ is the wavelength of the radar, R is the slant range from the satellite to the ground, θ is the incident angle of the radar, and B N is the vertical baseline.

[0085] Exemplarily, for the aforementioned 6 groups of baselines, 6 elevation ambiguities can be calculated according to the elevation ambiguity calculation formula.

[0086] In a specific implementation manner, for the elevation ambiguity between the primary satellite and the secondary satellites, the primary satellite can be used as the reference satellite; for the elevation ambiguity between the secondary satellites, any one of the secondary satellites can be used as the reference satellite. Among them, the orbital parameters in the xml metadata file collected by the reference satellite will be used to calculate the elevation ambiguity, and the orbital parameters are also the wavelength of the radar, the slant range from the satellite to the ground, the incident angle of the radar, etc.

[0087] (2.2) Compare the elevation ambiguities corresponding to each group of baselines to screen out multiple groups of target baselines from each group of baselines according to the comparison results.

[0088] Exemplarily, compare the 6 elevation ambiguities. For two or more elevation ambiguities with similar values, any one of the baselines corresponding to the elevation ambiguities can be selected as the target baseline. For example, if the elevation ambiguity corresponding to the baseline between the primary satellite A and the secondary satellite B is close to the elevation ambiguity corresponding to the baseline between the primary satellite A and the secondary satellite C, then the baseline between the primary satellite A and the secondary satellite B can be used as the target baseline, or the baseline between the primary satellite A and the secondary satellite C can be used as the target baseline.

[0089] Exemplarily, six elevation ambiguities are compared, and two sets of target baselines are selected, including the baseline between the primary satellite A and the secondary satellite B, and the baseline between the secondary satellite C and the secondary satellite D.

[0090] (3) Generation of unwrapped interferogram: For each set of target baselines, an interferogram corresponding to the target baseline is generated based on the single-look complex data collected by the two satellites to which the target baseline belongs.

[0091] The unwrapped interferogram is an interferogram obtained by removing the phase effects caused by terrain and orbital geometry during the generation of interferometric SAR, including two parts: interferogram calculation and removal of flat-earth phase.

[0092] The interferogram is obtained by multiplying the SAR complex images of two acquisitions pixel by pixel. This phase difference includes both the phase information caused by terrain, orbital geometry, etc., and signals such as surface deformation. Removing the orbital geometry phase from the interferogram is called "removing the flat-earth phase". The calculation formula is as follows:

[0093]

[0094] where B N is the vertical baseline, h(x, y) is the elevation, λ is the radar wavelength, R is the slant range from the satellite to the ground, and θ is the radar incidence angle.

[0095] Exemplarily, for the aforementioned two sets of target baselines, two corresponding interferograms can be generated.

[0096] After removing the orbital phase, the interferogram mainly retains the terrain elevation information and signals such as surface deformation. However, due to the advantage of the "wheel-type" formation, the time baseline during one acquisition is 0, there is no phase of surface deformation, nor is there an atmospheric phase difference between two acquisitions. Therefore, what is mainly retained is the surface elevation information. To further improve the clarity of the phase information, the unwrapped interferogram can be filtered, such as using methods like the Goldstein filter to reduce the noise impact.

[0097] (4) Multi-baseline phase unwrapping: Through the multi-baseline phase unwrapping model set, the interferogram corresponding to each set of target baselines is subjected to phase unwrapping processing to obtain the target unwrapped phase.

[0098] In practical applications, since the "wheel-type" formation can obtain six baselines of different lengths in one acquisition, after baseline optimization, the baseline pairs with uniformly distributed lengths of the acquired data (i.e., the aforementioned target baselines) can be phase-unwrapped through various multi-baseline phase unwrapping algorithms such as coarse-to-fine, maximum likelihood estimation, and maximum a posteriori estimation, and finally the target unwrapped phase is obtained.

[0099] In one implementation manner, it specifically includes:

[0100] (4.1) For each multi-baseline phase unwrapping model in the multi-baseline phase unwrapping model set, perform phase unwrapping processing on the interferogram corresponding to each group of target baselines respectively to obtain the initial unwrapped phase output by each multi-baseline phase unwrapping model.

[0101] Among them, the multi-baseline phase unwrapping model set includes a coarse-to-fine model, a maximum likelihood estimation model, and a maximum a posteriori estimation model. Specifically:

[0102] The coarse-to-fine method first unwraps the phase at a lower resolution or large scale, and then gradually enters a higher resolution or detailed level. Its advantage is to first solve the large-scale phase ambiguity problem and then process local details, thereby reducing the complexity and error risk of phase unwrapping.

[0103] The maximum likelihood estimation (MLE) method is an estimation method based on a statistical model. It assumes that the noise distribution of the interferogram is known and estimates the optimal solution of the phase by maximizing the likelihood function. It can effectively handle noise interference and provide more reliable unwrapping results, especially suitable for interferograms with a high noise level.

[0104] The maximum a posteriori estimation (MAP) method combines prior information and observed data. It not only uses the phase information in the interference data but also introduces a prior topographic or deformation model, thereby balancing the reliability of the data and prior constraints during the unwrapping process. The MAP method performs well when the terrain changes are complex or the noise in the interferogram is large.

[0105] Through these multi-baseline phase unwrapping algorithms, making full use of multiple groups of baselines with different lengths for data, the phase ambiguity problem can be solved at multiple scales.

[0106] (4.2) Determine the difference between each initial unwrapped phase and the preset unwrapped phase threshold respectively; among them, the preset unwrapped phase threshold is obtained by back-calculating using the known surface elevation corresponding to the target area.

[0107] (4.3) Determine the initial unwrapped phase corresponding to the smallest difference as the target unwrapped phase.

[0108] Finally, the target unwrapped phase can accurately reflect the height change or deformation information of the surface, providing important basic data for the generation of a high-precision digital elevation model.

[0109] In another implementation manner, a target multi-baseline phase unwrapping model can also be determined in advance from the multi-baseline phase unwrapping model set, and the target unwrapped phase can be obtained by performing phase unwrapping processing on the interferogram corresponding to each group of target baselines through this target multi-baseline phase unwrapping model.

[0110] (5) Extraction of surface elevation: Convert the target unwrapped phase into the target surface elevation corresponding to the target area.

[0111] In the specific implementation, the linear relationship between the phase and the terrain elevation in the interferometric geometry is utilized. The formula is as follows:

[0112]

[0113] where h is the relative elevation, B N is the vertical baseline, λ is the wavelength of the radar, R is the slant range from the satellite to the ground, θ is the incident angle of the radar, is the interferometric phase after removing the flat-earth phase (i.e., the target unwrapped phase). The target unwrapped phase provides the relative elevation. Therefore, when generating the absolute elevation, a reference elevation value needs to be introduced. The relative elevation can be corrected by using known ground control points (GCPs). For example, the sum of the reference elevation value corresponding to the known ground control points and the relative elevation is used as the absolute elevation to ensure that the target unwrapped phase matches the true terrain.

[0114] (6) Extraction of terrain information: Extract the first initial water body area from the target area according to the target surface elevation. The slope of the water body area is usually small, especially for the water bodies in lakes and plains. Therefore, the target surface elevation is used to calculate the slope, and the first initial water body area is extracted by identifying the low-slope areas. Specifically, it includes:

[0115] (6.1) Determine the slope value of each pixel in the target area using the target surface elevation. The slope value is calculated specifically according to the following formula:

[0116]

[0117] where S is the slope value, z is the target surface elevation, and are the lateral and longitudinal change rates of the target surface elevation respectively. According to the extracted slope, set the slope range of the water body. Usually, the slope of the water body is very small, and the set slope threshold also needs to be relatively small.

[0118] (6.2) Judge whether the slope value of the pixel is within the preset slope range;

[0119] (6.3) If so, determine that the pixel belongs to the suspected water body pixel; if not, determine that the pixel does not belong to the suspected water body pixel to extract the first initial water body area in the target area.

[0120] (7) Calculation of coherence coefficient: Extract the initial coherence coefficient between any two satellites within the multi-satellite formation according to the single-look complex data.

[0121] In specific implementation, the coherence coefficient of SAR image data is an important indicator for evaluating the similarity between two SAR image data. The value range of the coherence coefficient is between 0 and 1, where 1 indicates that the two SAR image data are completely coherent, and 0 indicates complete incoherence. The calculation formula is as follows:

[0122]

[0123] where γ is the initial coherence coefficient, S 1 and S 2 are the pixel values of two complex images, is the complex conjugate of S 2 , and <·> represents the average operation within a local window.

[0124] Exemplarily, according to the above formula, the initial coherence coefficients between the main satellite A and the secondary satellite B, between the main satellite A and the secondary satellite C, between the main satellite A and the secondary satellite D, between the secondary satellite B and the secondary satellite C, between the secondary satellite B and the secondary satellite D, and between the secondary satellite C and the secondary satellite D can be estimated, a total of 6 initial coherence coefficients.

[0125] (VIII) Coherence coefficient weighted fusion:

[0126] Since the "wheel-type" formation is a multi-satellite formation, the coherences extracted from different length baselines are also different. To ensure the unity of the results, weighted fusion needs to be performed relying on the elevation ambiguity. In the area with a larger elevation ambiguity, the phase change is less sensitive to the terrain, and the coherence is less affected by the terrain. Therefore, a higher fusion weight can be assigned. On the contrary, in the area with a smaller elevation ambiguity, due to the higher sensitivity to the phase, more errors may be introduced, so the fusion weight is lower. Specifically, it includes:

[0127] (8.1) From multiple initial coherence coefficients, screen out the initial coherence coefficients between the two satellites belonging to multiple groups of target baselines.

[0128] Exemplarily, 2 groups of target baselines include the baseline between the main satellite A and the secondary satellite B, and the baseline between the secondary satellite C and the secondary satellite D. On this basis, the initial coherence coefficients between the main satellite A and the secondary satellite B, and between the secondary satellite C and the secondary satellite D can be screened out for the subsequent fusion of multiple coherence coefficients.

[0129] (8.2) Determine the fusion weight based on the elevation ambiguities corresponding to multiple groups of target baselines. Specifically, the fusion weight can be determined according to the following formula:

[0130]

[0131] where W(hamb ) is the fusion weight, h amb is the elevation ambiguity, that is, the smaller the elevation ambiguity, the smaller the fusion weight.

[0132] (8.3) Using the fusion weight, fuse the selected initial coherence coefficients to obtain the target coherence coefficient.

[0133] In a specific implementation, to avoid numerical problems, a normalization factor can be introduced when calculating the target coherence coefficient, or other suitable non-linear functions (such as exponential decay functions) can be selected.

[0134] Exemplarily, taking the introduction of the normalization factor as an example, let the initial coherence coefficient between the main satellite A and the secondary satellite B be γ 1 , and the initial coherence coefficient between the secondary satellite C and the secondary satellite D be γ 2 , and the corresponding fusion weights be W 1 and W 2 , then the target coherence coefficient is determined according to the following formula:

[0135]

[0136] where γ fused is the target coherence coefficient.

[0137] (IX) Coherence threshold setting:

[0138] In practical applications, select a suitable coherence threshold according to the regional characteristics. For relatively calm water bodies, such as lakes or reservoirs, the coherence value may be slightly higher. For water bodies with obvious fluctuations, such as the ocean and rivers, the coherence is usually low. The threshold is selected by analyzing the histogram of the coherence image to find the region where the coherence values of pixels are concentrated. This part usually corresponds to water bodies.

[0139] (X) Low coherence region extraction:

[0140] In a specific implementation, determine whether the target coherence coefficient of each pixel in the target region is lower than the preset coherence threshold; if so, determine that the pixel belongs to a suspected water body pixel; if not, determine that the pixel does not belong to a suspected water body pixel, so as to extract the second initial water body region in the target region.

[0141] In the embodiment of the present invention, according to (IX), select the coherence threshold, generate a vector file of the region below the coherence threshold as the to-be-selected second initial water body region.

[0142] (XI) Fusing the terrain information and the low coherence region to obtain the target water body region: Fuse the first initial water body region and the second initial water body region to extract the target water body region in the target region.

[0143] In specific implementation, since the water body obtained only relying on the terrain slope information has high efficiency, in areas with relatively complex terrain, it is more effective than the simple elevation threshold method. However, relying only on the slope may misidentify flat non-water body areas, such as plains and farmlands. On the flat terrain of non-water bodies, the coherence of the "wheel-type" formation satellite data is extremely high, and water bodies and flat lands can be very accurately distinguished. And relying only on coherence for extraction is likely to identify shadows and layover phenomena in complex areas as water bodies, while it is very easy to distinguish them on the terrain. Therefore, by fusing the first initial water body area and the second initial water body area and taking their intersection, the water body can be very accurately identified and extracted, and the target water body area within the target area can be obtained.

[0144] In summary, the water body extraction provided by the embodiments of the present invention has at least the following characteristics:

[0145] (a) Low-cost large-scale water body extraction: Compared with traditional methods, the "wheel-type" formation can effectively extract water body information in one shot. The satellite image has a large coverage area and low cost, and is suitable for large-scale water body census.

[0146] (b) Can effectively identify water bodies under various terrains: By combining terrain information and coherence information, it makes up for the misidentification of flat non-water body areas only using terrain information and the misidentification of layover and shadow areas only using coherence information, combines the advantages of both, and greatly improves the traditional extraction accuracy.

[0147] (c) Fast processing speed, high accuracy, suitable for emergency disaster relief: Through the "wheel-type" formation satellite data for water body extraction, the latest terrain information and water body information can be obtained from the data of one shot, and it is not affected by clouds and fog and night. The processing speed is fast, and the accuracy of water body extraction is high, which is suitable for emergency disaster relief processing during floods.

[0148] Based on the foregoing embodiments, the embodiments of the present invention provide a water body extraction device. Refer to Figure 3 the structural schematic diagram of a water body extraction device shown, and the device mainly includes the following parts:

[0149] A data acquisition module 302, configured to acquire single-look complex data collected by each satellite in the multi-satellite formation for the target area;

[0150] A first water body extraction module 304, configured to estimate and screen the baselines between any two satellites in the multi-satellite formation according to the single-look complex data to obtain multiple groups of target baselines, and extract the first initial water body area within the target area based on the multiple groups of target baselines;

[0151] The second water body extraction module 306 is configured to extract the initial coherence coefficient between any two satellites within the multi-satellite formation according to the single-look complex data, fuse multiple initial coherence coefficients by using multiple sets of target baselines to obtain the target coherence coefficient, and extract the second initial water body area within the target area based on the target coherence coefficient;

[0152] The target water body extraction module 308 is configured to fuse the first initial water body area and the second initial water body area to extract the target water body area within the target area.

[0153] The water body extraction device provided by the embodiment of the present invention extracts the first initial water body area within the target area based on multiple sets of target baselines; at the same time, it fuses the initial coherence coefficients between any two satellites within the multi-satellite formation by using multiple sets of target baselines, and extracts the second initial water body area within the target area based on this; the two initial water body areas are fused to determine the target water body area within the target area. The present invention combines baseline information and coherence information to make up for the misidentification of flat non-water areas when only using baseline information and the misidentification of layover and shadow areas when only using coherence information, thereby significantly improving the accuracy of the water body extraction result.

[0154] In one implementation manner, the first water body extraction module 304 is specifically configured to:

[0155] For any two satellites within the multi-satellite formation, based on the orbital parameters when the two satellites respectively collect single-look complex data, estimate a set of baselines between the two satellites and their corresponding elevation ambiguities;

[0156] Compare the elevation ambiguities corresponding to each set of baselines to screen out multiple sets of target baselines from each set of baselines according to the comparison result.

[0157] In one implementation manner, the first water body extraction module 304 is specifically configured to:

[0158] For each set of target baselines, generate an interferogram corresponding to the target baseline based on the single-look complex data collected by the two satellites to which the target baseline belongs;

[0159] Perform phase unwrapping processing on the interferogram corresponding to each set of target baselines through a multi-baseline phase unwrapping model set to obtain the target unwrapped phase;

[0160] Convert the target unwrapped phase into the target surface elevation corresponding to the target area;

[0161] Extract the first initial water body area from the target area according to the target surface elevation.

[0162] In one implementation manner, the first water body extraction module 304 is specifically configured to:

[0163] For each multi-baseline phase unwrapping model in the multi-baseline phase unwrapping model set, perform phase unwrapping processing on the interferogram corresponding to each group of target baselines respectively to obtain the initial unwrapped phase output by each multi-baseline phase unwrapping model;

[0164] Determine the difference between each initial unwrapped phase and a preset unwrapped phase threshold respectively; wherein, the preset unwrapped phase threshold is obtained by back-inferring using the known surface elevation corresponding to the target area;

[0165] Determine the initial unwrapped phase corresponding to the smallest difference as the target unwrapped phase.

[0166] In one implementation manner, the first water body extraction module 304 is specifically configured to:

[0167] Use the target surface elevation to determine the slope value of each pixel in the target area;

[0168] Judge whether the slope value of the pixel is within a preset slope range;

[0169] If so, determine that the pixel belongs to a suspected water body pixel; if not, determine that the pixel does not belong to a suspected water body pixel, so as to extract the first initial water body area in the target area.

[0170] In one implementation manner, the second water body extraction module 306 is specifically configured to:

[0171] Select the initial coherence coefficients between the two satellites to which the multi-group target baselines belong from multiple initial coherence coefficients;

[0172] Determine the fusion weight based on the elevation ambiguity corresponding to the multi-group target baselines;

[0173] Use the fusion weight to fuse the selected initial coherence coefficients to obtain the target coherence coefficient.

[0174] In one implementation manner, the second water body extraction module 306 is specifically configured to:

[0175] Judge whether the target coherence coefficient of each pixel in the target area is lower than a preset coherence threshold;

[0176] If so, determine that the pixel belongs to a suspected water body pixel; if not, determine that the pixel does not belong to a suspected water body pixel, so as to extract the second initial water body area in the target area.

[0177] For the device provided by the embodiments of the present invention, the implementation principle and the generated technical effects are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding contents in the foregoing method embodiments.

[0178] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device; a computer program is stored on the storage device, and when the computer program is run by the processor, it executes the method described in any one of the above-described embodiments.

[0179] Figure 4 FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device 100 includes: a processor 40, a memory 41, a bus 42, and a communication interface 43. The processor 40, the communication interface 43, and the memory 41 are connected through the bus 42; the processor 40 is configured to execute an executable module stored in the memory 41, such as a computer program.

[0180] Among them, the memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 43 (which may be wired or wireless), a communication connection between the system network element and at least one other network element is realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0181] The bus 42 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 4 only a bidirectional arrow is used in FIG. 4, but it does not mean that there is only one bus or one type of bus.

[0182] Among them, the memory 41 is used to store a program. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device defined by the flow process disclosed in any one of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0183] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 40 or the instructions in the form of software. The above-mentioned processor 40 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and combines its hardware to complete the steps of the above method.

[0184] The computer program product of the readable storage medium provided by the embodiments of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For the specific implementation, reference can be made to the foregoing method embodiments, which will not be elaborated herein.

[0185] If the above-mentioned functions are implemented in the form of software functional units 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 the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0186] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments or easily conceive of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A water extraction method, characterized in that: include: Obtain single-view complex data collected by each satellite in the multi-satellite formation for the target area; According to the single-view complex data, the baselines between any two satellites in the multi-satellite formation are estimated and screened to obtain multiple groups of target baselines, and a first initial water body area in the target area is extracted based on the multiple groups of target baselines; Extracting an initial coherence coefficient between any two satellites in the multi-satellite formation according to the single-view complex data, fusing a plurality of the initial coherence coefficients using a plurality of sets of the target baselines to obtain a target coherence coefficient, and extracting a second initial water body region in the target region based on the target coherence coefficient; The first initial water body region and the second initial water body region are merged to extract a target water body region within the target region.

2. The water extraction method according to claim 1, characterized in that: According to the single-view complex data, the baselines between any two satellites in the multi-satellite formation are estimated and screened to obtain multiple groups of target baselines, including: For any two satellites in the multi-satellite formation, estimating a set of baselines and their corresponding elevation ambiguities between the two satellites based on the orbital parameters of the two satellites when they respectively collect the single-view complex data; The elevation ambiguities corresponding to each group of the baselines are compared, so as to screen out multiple groups of target baselines from each group of the baselines according to the comparison results.

3. The water extraction method according to claim 1, characterized in that: Extracting a first initial water body area in the target area based on multiple groups of target baselines includes: For each group of the target baselines, based on the single-view complex data respectively collected by the two satellites to which the target baseline belongs, an interferogram corresponding to the target baseline is generated; Through a multi-baseline phase unwrapping model set, phase unwrapping processing is performed on the interference pattern corresponding to each group of the target baselines to obtain a target unwrapping phase; Converting the target unwrapped phase into a target surface elevation corresponding to the target area; A first initial water body area is extracted from the target area according to the target surface elevation.

4. The water extraction method according to claim 3, characterized in that: By using a multi-baseline phase unwrapping model set, phase unwrapping processing is performed on the interference pattern corresponding to each group of the target baselines to obtain a target unwrapped phase, including: By using each multi-baseline phase unwrapping model in the multi-baseline phase unwrapping model set, the interferogram corresponding to each group of the target baselines is subjected to phase unwrapping processing to obtain an initial unwrapping phase output by each multi-baseline phase unwrapping model; Determine the difference between each of the initial unwrapping phases and a preset unwrapping phase threshold, respectively; wherein the preset unwrapping phase threshold is obtained by inverse calculation using the known surface elevation corresponding to the target area; The initial unwrapping phase corresponding to the smallest difference is determined as the target unwrapping phase.

5. The water extraction method according to claim 3, characterized in that: Extracting a first initial water body area from the target area according to the target surface elevation includes: Determine the slope value of each pixel in the target area using the target surface elevation; Determining whether the slope value of the pixel is within a preset slope range; If yes, it is determined that the pixel belongs to a suspected water body pixel; if no, it is determined that the pixel does not belong to a suspected water body pixel, so as to extract the first initial water body area in the target area.

6. The water extraction method according to claim 1, characterized in that: Using multiple groups of the target baselines to fuse the multiple initial coherence coefficients to obtain a target coherence coefficient includes: Screening out, from the plurality of the initial coherence coefficients, the initial coherence coefficients between the two satellites to which the plurality of groups of the target baselines belong; Determining a fusion weight based on the elevation ambiguities corresponding to the plurality of groups of target baselines; The selected initial coherence coefficients are fused using the fusion weights to obtain a target coherence coefficient.

7. The water extraction method according to claim 1, characterized in that: Extracting a second initial water body region within the target region based on the target coherence coefficient includes: Determining whether the target coherence coefficient of each pixel in the target area is lower than a preset coherence threshold; If yes, it is determined that the pixel belongs to a suspected water body pixel; if no, it is determined that the pixel does not belong to a suspected water body pixel, so as to extract the second initial water body area in the target area.

8. A water extraction device, characterized in that: include: A data acquisition module, used to acquire single-view complex data collected by each satellite in the multi-satellite formation for the target area; A first water body extraction module is used to estimate and screen the baselines between any two satellites in the multi-satellite formation according to the single-view complex data to obtain multiple groups of target baselines, and extract a first initial water body area in the target area based on the multiple groups of target baselines; A second water body extraction module is used to extract an initial coherence coefficient between any two satellites in the multi-satellite formation according to the single-view complex data, fuse multiple initial coherence coefficients using multiple groups of target baselines to obtain a target coherence coefficient, and extract a second initial water body area in the target area based on the target coherence coefficient; The target water body extraction module is used to merge the first initial water body area and the second initial water body area to extract the target water body area within the target area.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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