A Method and System for Shallow Water Underwater Topography Inversion Based on Video SAR

Abstract

This invention provides a method and system for shallow water underwater topography inversion based on video SAR, belonging to the fields of synthetic aperture radar signal processing and marine remote sensing applications. The method includes: acquiring multiple frames of SAR data with wave characteristics of a shallow water area based on video SAR; registering the acquired SAR data with these characteristics to obtain video SAR images; dividing the video SAR images into uniformly overlapping sub-image blocks using every two frames, and estimating the wave wavelength and wave velocity of each sub-image block; applying a linear dispersion relation to invert the water depth corresponding to all sub-image blocks, obtaining multiple sets of underwater topography inversion results; and fusing these multiple sets of underwater topography inversion results as the final underwater topography detection result for the study area. This invention enables more refined water depth inversion.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of Synthetic Aperture Radar (SAR) signal processing and ocean remote sensing application, and specifically relates to a shallow underwater terrain inversion method and system based on video SAR. BACKGROUND

[0002] Shallow underwater terrain inversion is a key scientific task, which is of great significance for ocean resource development, shallow navigation safety, and dynamic topography research. Shallow underwater terrain structure is complex and diverse, including sand bars, sandbanks, and reefs. These terrains may change significantly in a short time under the influence of natural events such as storms and tsunamis. In the prior art, a method based on remote sensing image sea wave features is widely used. This method uses the interaction between sea waves and seabed terrain (such as refraction, diffraction, reflection, and breaking) to infer underwater terrain. By analyzing the changes in wave length, wave direction, and other parameters, combined with linear dispersion relationship, the shallow underwater terrain can be inverted. However, this method has some limitations in practical application:

[0003] 1. Wave period assumption problem: The existing technology uses sea wave features to invert water depth based on the assumption that the wave period is constant. However, the wave period of actual shallow sea changes constantly with the propagation. This assumption does not match the actual situation, resulting in high inversion error.

[0004] 2. Insufficient detail resolution: When using wave length and wave period to invert water depth, the sub-image block is required to contain multiple wave periods, and the inverted water depth value is the average water depth of the sub-image block. This method is difficult to reflect the finer water depth changes and cannot meet the needs of high-precision terrain inversion.

[0005] 3. Limitations of single SAR image: Existing research mainly uses single SAR data for water depth inversion, but single SAR image is easily disturbed by other phenomena, resulting in unstable inversion results and difficulty in meeting the needs of practical application. SUMMARY

[0006] To solve the above technical problems, the present application provides a shallow sea underwater terrain inversion method and system based on video SAR, to improve the shallow sea underwater terrain inversion capability based on wave characteristics. First, based on the relationship between wavelength, wave period and wave speed, the wavelength and wave speed are calculated based on video SAR data, solving the problem of relying on fixed wave period. Second, based on wave speed and wavelength, it is more desirable to invert water depth than based on wave period and wavelength, and wave speed estimation does not require sub-image blocks to contain several wave periods, and can reflect more detailed water depth changes. Finally, since single-scene SAR images cannot achieve accurate wave speed estimation, video SAR imaging provides support for accurate wave speed estimation, and fusion of water depth inversion results of every two frames of video SAR data can improve the stability and accuracy of underwater terrain inversion, and achieve more refined water depth inversion.

[0007] To achieve the above purpose, the present application adopts the following technical scheme:

[0008] A shallow sea underwater terrain inversion method based on video SAR, comprising the following steps:

[0009] Step 1, obtaining multiple frames of SAR data with wave characteristics in the shallow sea area based on video SAR;

[0010] Step 2, registering the obtained multiple frames of SAR data with characteristics in the shallow sea area to obtain video SAR images;

[0011] Step 3, dividing sub-image blocks uniformly and with overlap using every two frames of video SAR images, and respectively estimating the wave length and wave speed of the sub-image blocks;

[0012] Step 4, applying linear dispersion relationship to invert the water depth corresponding to all sub-image blocks to obtain multiple sets of underwater terrain inversion results;

[0013] Step 5, fusing the multiple sets of underwater terrain inversion results as the final underwater terrain detection results of the research area.

[0014] The present application also provides a video SAR shallow sea underwater terrain inversion system, comprising:

[0015] A video SAR data acquisition module acquires SAR data with wave characteristics in the shallow sea area based on video SAR;

[0016] A video SAR data registration module registers the obtained multiple frames of SAR data with characteristics in the shallow sea area to obtain video SAR images;

[0017] A wave parameter estimation module divides sub-image blocks uniformly and with overlap using every two frames of video SAR images, and respectively estimates the wave length and wave speed of the sub-image blocks;

[0018] The water depth inversion module applies a linear dispersion relation to invert water depths corresponding to all sub-image blocks to obtain a plurality of sets of underwater topography inversion results.

[0019] The water depth fusion module fuses the plurality of sets of underwater topography inversion results as final underwater topography detection results of the research area.

[0020] The application further provides an electronic device, comprising:

[0021] one or more processors;

[0022] a memory for storing one or more programs;

[0023] wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned shallow sea underwater topography inversion method based on video SAR.

[0024] The application further provides a computer-readable storage medium having stored executable instructions, which, when executed by a processor, enable the processor to implement the above-mentioned shallow sea underwater topography inversion method based on video SAR.

[0025] Advantages:

[0026] The prior art assumes that the wave period is constant, but in actual sea wave propagation, the wave period changes, which limits the accuracy of water depth inversion. Therefore, the video SAR imaging technology has the following advantages: the relationship between wavelength, wave period and wave speed is constant, the video SAR image can accurately estimate the wave speed, thereby breaking the dependence on the fixed wave period. The wave speed estimation has low requirements for sub-image blocks, only the feature points are required, and the water depth changes can be more accurately distinguished. Therefore, the application uses video SAR data to realize water depth inversion by accurately estimating the wave speed and wavelength, solves the problem of dependence on the fixed wave period in the prior art, and has the following significant advantages:

[0027] (1) The application fuses the underwater topography inverted by a plurality of frames of SAR data based on mean fusion, Wiener filter fusion and Kalman filter fusion algorithms, effectively improves the stability of the underwater topography inversion result, significantly weakens the influence of other interference factors on the inversion accuracy, thereby enhancing the robustness of the algorithm, and showing wide application potential.

[0028] (2) The application uses the maximum cross-correlation method to track the sea waves, calculates the corresponding wave speed, fully utilizes the differences between different frames of data, and the wave speed estimation does not require the sub-image block to contain several sea wave periods, only the feature points are required, and the changes caused by local water depth can be captured, thereby reducing the influence of window smoothing on the water depth inversion result.

[0029] (3) The present application estimates sea wave length based on cross spectrum, compared with two-dimensional Fourier transform, the real part of cross spectrum can represent sea wave length, and the imaginary part can represent sea wave direction, and the 180-degree ambiguity of wave direction can be removed. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A flow chart of the shallow sea underwater terrain inversion method based on video SAR of the present application;

[0031] Figure 2 An implementation principle diagram of the shallow sea underwater terrain inversion method based on video SAR of the present application;

[0032] Figure 3 A chart data schematic diagram of a research area provided by an embodiment of the present application;

[0033] Figure 4a Figure 4b A simulation video image example provided by the research area of an embodiment of the present application; wherein, Figure 4a A simulation sea surface corresponding to t=1s, Figure 4b A simulation sea surface corresponding to t=6s;

[0034] Figure 5 Wavelength estimation results provided by the research area of an embodiment of the present application;

[0035] Figure 6 Wave velocity estimation results provided by the research area of an embodiment of the present application;

[0036] Figure 7 Water depth inversion results provided by the research area of an embodiment of the present application;

[0037] Figure 8 Water depth fusion results provided by the research area of an embodiment of the present application;

[0038] Figure 9 A schematic diagram of the shallow sea underwater terrain inversion system based on video SAR of the present application. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0040] As shown in Figure 1 The present application provides a shallow sea underwater terrain inversion method based on video SAR, including the following steps:

[0041] ​Step 1, acquiring SAR data with sea wave features in shallow sea area based on video SAR;

[0042] Step 2, registering the acquired multi-frame SAR data to obtain video SAR images;

[0043] Step 3, dividing the sub-image blocks uniformly and with overlap from each two SAR images, and respectively estimating sea wave length and wave speed;

[0044] Step 4, applying linear dispersion relation to invert the water depth corresponding to all sub-image blocks to obtain multiple sets of underwater topography inversion results;

[0045] Step 5, fusing the multiple sets of underwater topography inversion results as the final underwater topography detection results of the research area.

[0046] Specifically, the step 1 comprises:

[0047] In some embodiments, a research area is selected, which is often difficult to implement in-situ measurement due to frequent changes in underwater topography, and remote sensing data is needed to invert the underwater topography. The sea wave feature change is a common method for shallow sea underwater topography detection, so first, SAR imaging is performed on the shallow sea area based on video SAR to acquire SAR data with clear sea wave features.

[0048] Specifically, the step 2 comprises:

[0049] First, the SAR data is pre-processed, mainly including four steps of radiation calibration, slant conversion, resampling and LEE filtering, and the video SAR data is registered to obtain the corresponding video SAR images for subsequent underwater topography inversion.

[0050] Specifically, the step 3 comprises:

[0051] Two SAR images are selected, and are divided into sub-image blocks uniformly and with overlap, , are all natural numbers greater than 1. Taking a pair of sub-image blocks in the same area as an example, based on formula (1), two-dimensional Fourier transform is performed on the sub-image block to obtain the corresponding image spectrum , where the image spectrum obtained by two-dimensional Fourier transform of the two sub-image blocks is represented by and .

[0052] (1)

[0053] where j represents an imaginary unit, are respectively The wave number in the direction, where the spectral resolution of the image spectrum is , , and are the lengths of the sub-image block in directions, respectively.

[0054] If the cross spectrum is used to calculate the wavelength, the corresponding cross spectrum needs to be calculated additionally using equation (2) :

[0055] (2)

[0056] where denotes the conjugate of a complex number.

[0057] The wave length of the sea wave can be calculated according to the spectral peak position of the image spectrum or the cross spectrum :

[0058] (3)

[0059] where and denote the peak point coordinates in the wave number domain space.

[0060] The wave speed of the sea wave can be tracked by the maximum cross-correlation method to calculate the corresponding wave speed. The first image is divided into template windows, which are consistent with the center of the sub-image block divided before, but the size is smaller because the wave speed estimation only needs to contain the feature points. One of the template windows is selected, and a larger region in the second image is used as a search window , where the center is located at the same position as the template window . By moving a search sub-window , which has the same size as the template window , up, down, left and right in the search window , the cross-correlation coefficient is calculated by cross-correlating the search sub-window and the template window

[0061] :

[0062] where and are the displacements of the search sub-window relative to the template window in the horizontal direction and the vertical direction, respectively, is the covariance of the search sub-window relative to the template window after moving to the right and upward, and Each is a search sub-window and template window The standard deviation. This represents the average value of the search sub-windows. This represents the average value of the template window.

[0063] Based on the maximum cross-correlation subwindow The displacement and the time interval between two frames Calculate the velocity components of ocean waves in the horizontal and vertical directions. :

[0064] (5)

[0065] (6)

[0066] in, and These are the points where the maximum number of cross-correlation numbers is obtained. Compared to Displacement to the right and upward (negative displacement to the left or downward). and These are the resolutions in the horizontal and vertical directions, respectively.

[0067] The corresponding wave speed c is:

[0068] (7)

[0069] Specifically, step 4 includes:

[0070] The wave dispersion relation is the wave wavelength. Wave speed With water depth The constraints between them. Based on ocean wave wavelength. and wave speed The estimated result is used to invert the corresponding water depth using formula (8).

[0071] (8)

[0072] in, Because of the water depth, Wavelength denoted as wave speed. g represents gravitational acceleration; atanh represents the inverse hyperbolic tangent function.

[0073] By calculating the wavelength and wave speed, the water depth of the corresponding sub-image is obtained using the wave dispersion relation. By traversing each pair of sub-images within the study area, the corresponding wavelength and wave speed are estimated, and the water depth at the center point of each pair of sub-images is obtained using the dispersion relation. The underwater topography of the entire study area is synthesized from all depth points:

[0074] (9)

[0075] in, Indicates the first Line number The position of the sub-image patch corresponding to the column Group water depth inversion results.

[0076] Specifically, step 5 includes:

[0077] The underwater topography of the corresponding study area can be retrieved based on two SAR images. Since this invention can acquire more than two SAR images during video SAR imaging, if this invention acquires... By inverting water depth multiple times between every two frames of SAR video images, the desired result can be obtained. Due to variations in sea state, the water depth inversion results based on wave characteristics also differ. To obtain more stable inversion results, this invention requires refining the inverted water depths within the study area. The results of the two water depths were merged.

[0078] Mean fusion, Wiener filter fusion, and Kalman filter fusion can all achieve the fusion of multiple sets of water depth inversion results. Among them, Kalman filter is an optimal linear recursive filter for time-domain processing proposed by Kalman. Its basic idea is to use the state estimate of the previous time step and the observation value of the current time step to obtain the optimal estimate of the state variables of the dynamic system at the current time step. It has good performance in dealing with nonlinear systems and non-Gaussian noise. Its state equation and observation equation are given by formula (10) and formula (11), respectively.

[0079] (10)

[0080] (11)

[0081] in, Represents the state vector. Represents the observation vector. yes Time's up The state transition matrix at time t, for The observation matrix at time, and They are unrelated noises.

[0082] Next, regarding the... Line number Inversion of sub-image patch positions in a column Group water depth results A depth fusion process is performed, using the depth as the observation vector for Kalman filtering. The final wave wavelength is calculated through prediction and update processes. First, the state vector from the previous time step is used... Obtain the estimated wave wavelength at the current moment. And calculate the error matrix. The prediction process is given by formulas (12) and (13) respectively. Then the gain matrix is ​​calculated. Update the state vector And calculate the error matrix. The update process is completed by formulas (14) to (16). The parameters assumed in this invention are as follows: , , and As a constant, through The water depth inversion results are continuously updated and predicted to obtain the final merged water depth. .

[0083] (12)

[0084] (13)

[0085] (14)

[0086] (15)

[0087] (16)

[0088] in, express The water depth is estimated at all times. yes Time's up The state transition matrix at time t, express The state vector at time t, Represents the error matrix. Let the gain matrix be denoted as . The observation matrix at time, express The observation vector at time t, and It's noise. and The covariance matrix. The superscript T denotes the transpose of the matrix.

[0089] The above process is repeated continuously to estimate the fusion water depth based on each location, thereby realizing the shallow sea underwater topography detection of the corresponding area, as given by formula (17).

[0090] (17)

[0091] in, Indicates the first Line number The fusion depth of the sub-image block positions in the column.

[0092] The following detailed description of the shallow water underwater topography inversion method and system based on video SAR proposed in this invention is provided through specific embodiments:

[0093] like Figure 2 As shown, the input is SAR data acquired in shallow sea areas based on video SAR. The SAR data is registered and preprocessed to obtain the corresponding video SAR image. Taking m frames as an example (i.e....) Figure 2 The first SAR image to the m-th SAR image are divided into sub-images for each pair of SAR images. The wave wavelength is estimated based on the cross spectrum or image spectrum, and the wave is tracked and the wave speed is calculated based on the maximum cross-correlation method. Figure 2 (1) Wave parameter estimation), based on wavelength and wave speed, and based on linear diffusion relation, the water depth of each submap is inverted (i.e. Figure 2 (2) Shallow water depth inversion), based on Kalman filtering and other methods to fuse the water depth of each sub-block (i.e. Figure 2 (3) Shallow water and deep water are integrated to obtain the final underwater topography of the study area.

[0094] First, this embodiment selects Rottnest Island, Australia as the study area, and downloads the reference underwater topography of this area, such as... Figure 3 As shown. Due to the scarcity of real video SAR data, this paper simulates the irregular wave propagation process by solving the Boussinesq equation, and uses simulated video data as an example to carry out shallow sea underwater topography inversion. Figure 4a , Figure 4b Two simulated sea surface examples are given.

[0095] Then, the SAR data is radiometrically calibrated, skewed to ground conversion is performed, resampling and LEE filtering are applied, and the video SAR data is registered to obtain the corresponding video SAR image.

[0096] Next, the image is divided into sub-image blocks. The cross spectrum of each pair of sub-image blocks is calculated based on formula (1)-formula (2), and the corresponding wave wavelength is calculated based on formula (3). Figure 4a , Figure 4b The wavelength estimation results of the two simulated images are derived from Figure 5 Provided.

[0097] The maximum cross-correlation method is used to track ocean waves. The cross-correlation coefficient is calculated based on formula (4). The offset of the template sub-window with the maximum cross-correlation coefficient is considered to be the most likely sea surface translation. The corresponding ocean wave speed is calculated based on formula (5)-formula (7). Figure 4a , Figure 4b The wave velocity results of the two simulated images are derived from Figure 6 Provided.

[0098] Based on the estimated wave wavelength and wave speed, the water depth is inverted using the linear dispersion relation given by formula (8). Figure 4a , Figure 4b The water depth inversion results of the two simulated images are derived from Figure 7 Provided.

[0099] Finally, based on formulas (10)-(16), the Kalman filter algorithm is used to fuse the water depth inversion results, as shown in the figure. Figure 8 As shown.

[0100] This completes the SAR shallow water underwater topographic survey.

[0101] like Figure 9 As shown, the present invention also provides a video SAR shallow water underwater topography inversion system for implementing the above method, comprising:

[0102] The video SAR data acquisition module acquires SAR data with wave characteristics in shallow sea areas based on video SAR.

[0103] The video SAR data registration module registers multiple frames of SAR data with characteristics of shallow sea areas to obtain video SAR images.

[0104] The wave parameter estimation module uses every two frames of video SAR image to divide the image into sub-image blocks that are uniform and overlapping, and estimates the wave wavelength and wave speed of each sub-image block.

[0105] The water depth inversion module applies linear diffusion relation to invert the water depth corresponding to all sub-image blocks, and obtains multiple sets of underwater topography inversion results;

[0106] The water depth fusion module integrates multiple sets of underwater topography inversion results and uses them as the final underwater topography detection results for the study area.

[0107] The present invention also provides an electronic device, comprising:

[0108] One or more processors;

[0109] Memory, used to store one or more programs;

[0110] Specifically, when one or more programs are executed by the one or more processors, the one or more processors implement the above-described shallow seawater topography inversion method based on video SAR.

[0111] The present invention also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the above-described method for shallow sea underwater topography inversion based on video SAR.

[0112] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented 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. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0113] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure One One or more processes and / or boxes Figure One A device that provides the functions specified in one or more boxes.

[0114] 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 One One or more processes and / or boxes Figure One The function specified in one or more boxes.

[0115] 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 OneOne or more processes and / or boxes Figure One The steps of the function specified in one or more boxes.

[0116] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0117] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for shallow water bathymetry inversion based on video SAR, characterized in that, The method comprises the following steps: Step 1, acquiring multiple frames of SAR data with wave features in a shallow sea area based on video SAR; Step 2, registering the acquired multiple frames of SAR data with features in the shallow sea area to obtain video SAR images; pre-processing the SAR data, including radiometric calibration, slant conversion, resampling and LEE filtering, and registering the SAR data to obtain corresponding video SAR images; Step 3, dividing sub-image blocks uniformly and with overlap from each two frames of video SAR images, and respectively estimating wave length and wave speed of the sub-image blocks, comprising: performing two-dimensional Fourier transform on each sub-image block to obtain an image spectrum, calculating cross spectrum of two sub-image blocks, and determining wave length by using a spectrum peak position; tracking the wave by using a maximum cross-correlation method, dividing the first frame of video SAR images into multiple template windows, and taking a region at a corresponding position in the second frame of video SAR images as a search window; moving a sub-window with a size same as that of the template window in the search window, calculating a cross-correlation coefficient, calculating speed components of the wave in horizontal and vertical directions according to a displacement corresponding to the maximum cross-correlation coefficient and a time interval of the two frames of video SAR images, and thus obtaining wave speed; Step 4, applying a linear dispersion relationship to invert water depths corresponding to all sub-image blocks to obtain multiple sets of underwater terrain inversion results; Step 5, fusing the multiple sets of underwater terrain inversion results to obtain final underwater terrain detection results of the research area.

2. The video-SAR based shallow-water bathymetry inversion method of claim 1, wherein, In the step 1, the shallow sea area is imaged by video SAR, and the shallow sea area has clear and visible wave features.

3. The video-SAR based shallow-water bathymetry inversion method of claim 1, wherein, In the step 4, based on wave length and wave speed, a linear dispersion relationship formula for a limited water depth is used to calculate water depths corresponding to each sub-image block, and thus multiple sets of underwater terrain inversion results are obtained.

4. The video-SAR based shallow-water bathymetry inversion method of claim 3, wherein, In the step 5, based on the multiple sets of underwater terrain inversion results at positions of each sub-image block, mean fusion, Wiener filter fusion or Kalman filter fusion is used for fusion processing to obtain final water depth inversion results.

5. A video SAR based shallow water bathymetry inversion system, characterized in that, The method comprises the following modules: a video SAR data acquisition module, which acquires SAR data with wave features in a shallow sea area based on video SAR; a video SAR data registration module, which registers multiple frames of SAR data with features in the shallow sea area to obtain video SAR images; pre-processes the SAR data, including radiometric calibration, slant conversion, resampling and LEE filtering, and registers the SAR data to obtain corresponding video SAR images; The sea wave parameter estimation module uses every two frames of video SAR images, divides the sub-image blocks uniformly and with overlap, and estimates the sea wave length and wave speed of the sub-image blocks respectively, including: performing two-dimensional Fourier transform on each sub-image block to obtain an image spectrum, calculating the cross spectrum of two sub-image blocks, and using the spectrum peak position to determine the sea wave length; using the maximum cross-correlation method to track the sea wave, dividing the first frame of video SAR images into multiple template windows, and using the corresponding position area in the second frame of video SAR images as a search window; moving a sub-window with the same size as the template window in the search window, calculating the cross-correlation coefficient, calculating the speed components of the sea wave in the horizontal and vertical directions according to the displacement corresponding to the maximum cross-correlation coefficient and the time interval of the two frames of video SAR images, and thus obtaining the wave speed; The water depth inversion module applies a linear dispersion relationship to invert the water depths corresponding to all the sub-image blocks, and obtains multiple groups of underwater topography inversion results; The water depth fusion module fuses the multiple groups of underwater topography inversion results, and takes the results as the final underwater topography detection results of the research area.

6. An electronic device, comprising: It comprises: one or more processors; a memory for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the shallow sea underwater topography inversion method based on video SAR according to any one of claims 1-5.

7. A computer readable storage medium characterized in that, It has executable instructions stored thereon, which, when executed by a processor, can enable the processor to implement the shallow sea underwater topography inversion method based on video SAR according to any one of claims 1-5.

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