A method for monitoring reclamation of a coast using satellite remote sensing

Abstract

The present application relates to the technical field of remote sensing monitoring, in particular to a method for monitoring reclamation of a coastal area by satellite remote sensing. The method comprises the following steps: obtaining a pair of SAR image data of a target monitoring coastal area for speckle noise suppression and range-doppler terrain correction processing. The present application calculates the coherence coefficient of the deformation area by obtaining the pixels in the main image and the secondary image in the processed SAR image pair data of the deformation area, judges whether the deformation area is a candidate reclamation change suspicious area within a set time range value by using the coherence coefficient of the deformation area and the coherence coefficient threshold value of the deformation area, and determines the candidate reclamation change suspicious area by focusing on the coherence coefficient of the deformation area. The present application avoids blind search of the entire monitoring range, accurately locates the specific position of the reclamation change, thereby reducing the possibility of misjudgment. Meanwhile, focusing on the coherence coefficient of the deformation area can quickly exclude most of the deformation areas without change, thereby improving the monitoring efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing monitoring, in particular to a method for monitoring reclamation of a coast by satellite remote sensing. BACKGROUND

[0002] SAR has the imaging ability of all-weather and all-day, and can penetrate clouds and fog, making up for the deficiency of optical remote sensing. However, the accuracy of using SAR images alone for feature classification and boundary extraction is relatively low, especially in a complex coastal area. In the process of obtaining SAR image data of the complex coastal area, the geometric accuracy is affected by satellite orbit data, so that the original orbit data has certain errors, resulting in a deviation between the feature position in the SAR image and the actual geographical position. In addition, the speckle noise is randomly distributed as granular noise on the SAR image, making the texture of the SAR image data fuzzy and complex, increasing the difficulty and error of identification. At the same time, there is geometric distortion in the process of obtaining SAR image data, which further causes the spatial position of the feature in the SAR image to deviate and deform, resulting in a deviation between the reclamation area on the SAR image and the actual geographical position in the space, which is easy to cause misjudgment or omission. Therefore, the speckle noise and geometric distortion of the SAR image bring challenges to the identification of the reclamation change suspicious area.

[0003] In general, the existing coast line monitoring technology is seriously interfered by weather conditions such as clouds and rain, and the speckle noise and geometric distortion in the image data reduce the reliability of the identification of the reclamation change suspicious area. At the same time, most of the existing technologies analyze the entire monitoring area, which not only increases the data processing amount and the calculation complexity, resulting in a decrease in monitoring accuracy, but also cannot accurately locate the change area of the reclamation activity, which has the possibility of misjudgment, thereby causing the problem of low monitoring efficiency. Therefore, there is an urgent need for a more efficient and accurate method for monitoring reclamation of a coast by satellite remote sensing. SUMMARY

[0004] The present application aims to provide a method for monitoring reclamation of a coast by satellite remote sensing to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides a method for monitoring reclamation of a coast by satellite remote sensing, characterized by comprising the following method steps:

[0006] S1, obtaining image pair data of different viewing angles of a target monitoring coastal area at consecutive time sequences and processing the image pair data;

[0007] S2, acquire the processed image pair data, and perform conjugate multiplication as the main image and the auxiliary image to generate an interferogram, form a deformation map and judge the deformation area, wherein: the high-pixel image data is taken as the main image data, and the low-pixel image data is taken as the auxiliary image data; based on the window size of the deformation area, the pixels and the number of pixels of the main image and the auxiliary image are extracted, the coherence coefficient of the deformation area is calculated to judge the candidate reclamation change suspicious area within the set time range value; the main image and the auxiliary image pixels of the candidate reclamation change suspicious area are extracted to capture the change of the ground texture, and the filling material of the candidate reclamation change suspicious area is identified;

[0008] S3, acquire the corresponding area of the candidate reclamation change suspicious area in the interferogram and intercept, perform a morphological opening operation on the intercepted candidate suspicious area of the interferogram, identify the reclamation change suspicious area, and reclamation the reclamation change suspicious area according to the type of filling material.

[0009] As a further improvement of the technical solution, S2.1 in S2, generating an interferogram to remove the phase difference after the terrain phase includes the following method steps, and the following method steps are implemented:

[0010] The processed SAR image pair data is taken as the main image and the auxiliary image respectively, the pixels of the auxiliary image are aligned with the main image, the pixel-level matching is ensured, so as to obtain the two image data after matching and perform conjugate multiplication to generate an interferogram, record the phase of the processed SAR image pair data to calculate the phase difference of the interferogram, obtain the radar wavelength, the vertical baseline, the slant range, the incidence angle and the terrain height through the processed SAR image pair data, calculate the terrain phase, and calculate the phase difference after removing the terrain phase by using the phase difference of the interferogram and the terrain phase.

[0011] As a further improvement of the technical solution, S2.1 in S2, forming a deformation map through the ground deformation variable, and automatically discriminating the deformation area through the deformation map, so as to realize the following method steps for determining that the spatial range of the reclamation activity has changed, and the following method steps are implemented:

[0012] The ground deformation variable is stored as a matrix with the same size as the processed SAR image pair data, a deformation variable matrix is formed and visualized as an image, and then a deformation map is formed, and whether there is a deformation area in the deformation map is judged by using the ground deformation variable; when the ground deformation variable is greater than 0, the area in the deformation map has obvious ground uplift, it is determined that there is a deformation area in the deformation map; when the ground deformation variable is less than 0, the area in the deformation map has obvious ground subsidence, it is determined that there is a deformation area in the deformation map, and if there is a deformation area in the deformation map, it is determined that the spatial range of the reclamation activity has changed.

[0013] As a further improvement of the technical solution, S2.2 in S2 implements the following method steps to automatically identify the candidate reclamation change suspected area, and implements the following method steps:

[0014] By obtaining the pixels in the main image and the secondary image in the deformation area in the SAR image pair data processed, and extracting the number of pixels from the window size in the deformation area, the coherence coefficient of the deformation area is calculated by the pixels in the main image and the secondary image in the deformation area and the number of pixels. Set the time range value and the coherence coefficient threshold of the deformation area, use the coherence coefficient of the deformation area and the coherence coefficient threshold of the deformation area to judge whether the deformation area is a candidate reclamation change suspected area within the set time range value, record the number of times that the coherence coefficient of the deformation area is less than the coherence coefficient threshold of the deformation area, and the number of times that the coherence coefficient of the deformation area is greater than the coherence coefficient threshold of the deformation area, when the less number is higher than the greater number, it is determined that the deformation area exists within the set time range value. Sharp decline, the deformation area is a candidate reclamation change suspected area.

[0015] As a further improvement of the technical solution, S2.2 in S2 identifies the following method steps for uneven fill distribution range and fill material of the candidate reclamation change suspected area, and performs the following method steps:

[0016] Obtain the SAR image pair data and the preprocessed optical image data corresponding to the candidate reclamation change suspected area and fuse them, capture the change of the ground texture by analyzing the fused image data, and identify the uneven fill distribution range and fill material of the candidate reclamation change suspected area according to the change of the ground texture.

[0017] As a further improvement of the technical solution, S3 includes the following method steps for identifying the reclamation change suspected area:

[0018] The area of the candidate reclamation change suspicious area is calculated according to the uneven reclamation distribution range of the candidate reclamation change suspicious area, the actual coverage area of reclamation can be accurately defined through the targeted calculation of the area, the corresponding area in the interferogram is found through the candidate reclamation change suspicious area, the candidate suspicious area of the interferogram is intercepted, the corresponding area in the interferogram is directly located and intercepted through the candidate reclamation change suspicious area, large-scale processing of the entire interferogram is avoided, the data processing amount is reduced, the candidate suspicious area of the interferogram is converted into a binary image, the binary image of the candidate suspicious area is extracted, after the candidate suspicious area is converted into a binary image, the data structure is simpler, the subsequent morphological operation and connected region extraction are facilitated, and the processing speed and efficiency are improved; the connected region is formed through the operation of first corrosion and then inflation on the binary image of the candidate suspicious area, and the connected region greater than the area threshold value is retained, the connected region is the reclamation change suspicious area, and the reclamation change suspicious area is filled according to the reclamation material type and the area of the candidate reclamation change suspicious area, so that the development and utilization of reclamation resources can be reasonably planned, the area needing to be focused on and managed can be determined, and the waste of a large amount of reclamation resources can be avoided.

[0019] Compared with the prior art, the beneficial effects of the present application are:

[0020] 1. The method for monitoring the reclamation of a coast by using satellite remote sensing, the pixels in the main image and the auxiliary image in the deformation area in the processed SAR image pair data are obtained, the number of pixels in the window size in the deformation area is extracted, the coherence coefficient of the deformation area is calculated according to the pixels in the main image and the auxiliary image in the deformation area and the number of pixels, the deformation area is determined as a candidate reclamation change suspicious area by using the coherence coefficient of the deformation area and the coherence coefficient threshold value of the deformation area within a set time range, the candidate reclamation change suspicious area is determined by focusing on the coherence coefficient of the deformation area, so that blind search on the entire monitoring range is avoided, and the possibility of misjudgment is reduced, and meanwhile, focusing on the coherence coefficient of the deformation area can quickly exclude most of the deformation areas without change, and the monitoring efficiency is improved.

[0021] 2. The method for monitoring the reclamation of a coast by using satellite remote sensing, the atmospheric phase is estimated by using the processed SAR image pair data, the phase difference after removing the terrain phase is subtracted by the atmospheric phase, the difference phase is converted into a ground deformation variable, the ground deformation variable is stored as a matrix with the same size as the processed SAR image pair data, a deformation variable matrix is formed and visualized as an image, and then a deformation map is formed, the existence of a deformation area in the deformation map is determined by using the ground deformation variable, it is determined that the spatial range of reclamation activities has changed, the redundant calculation on irrelevant areas can be avoided through the deformation area of the deformation map, the data amount to be processed is greatly reduced, and the spatial range change of reclamation activities is accurately identified, so that the monitoring accuracy is improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] Fig. 1 is a whole step block diagram of the present application;

[0023] Fig. 2 is a flow block diagram of the present application for determining the spatial range variation of reclamation activities using deformation maps;

[0024] Fig. 3 is a flow block diagram of the present application for identifying candidate reclamation change suspicious areas for deformation areas. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0026] Embodiment 1

[0027] The present application provides a method for monitoring reclamation of a coastal area by satellite remote sensing. Referring to Figs. 1-3 , the method comprises the following method steps:

[0028] S1, obtaining image pair data of different view angles and continuous time series of a target monitoring coastal area and processing the same;

[0029] S1 comprises the following method steps:

[0030] S1.1, first, a target monitoring coastal area is selected, then SAR (synthetic aperture radar) image data of different view angles and continuous time series of the target monitoring coastal area are obtained by satellite remote sensing technology, so as to extract SAR image pair data (referring to combining SAR image data of two different view angles and continuous time series to form SAR image pair data), thereby ensuring high-frequency monitoring capability, meanwhile, high-resolution optical image data matching the time window of the SAR image data are obtained by satellite remote sensing technology, then the SAR image pair data are subjected to precise orbit data updating processing, so as to obtain SAR image pair data with updated precise orbit, then the SAR image pair data with updated precise orbit are subjected to radiometric calibration (converting DN value into backscattering coefficient value) processing, thereby obtaining radiometrically calibrated SAR image pair data, the radiometrically calibrated SAR image pair data are subjected to speckle noise suppression processing by Lee filtering, thereby obtaining SAR image pair data with processed noise, which reduces texture blurring and complexity of the SAR image pair data, thereby reducing identification difficulty and error;

[0031] The speckle noise suppression processing by Lee filtering comprises the following method steps:

[0032] Step 1, collect the radiometrically calibrated SAR image pair data, and then check the basic information of the radiometrically calibrated SAR image pair data, such as the size (number of rows and columns) of the SAR image pair, the number of bands, and whether there are missing values or abnormal values in the data type, if so, use appropriate methods (such as interpolation method, statistical method) for preprocessing, and obtain the preprocessed SAR image pair data;

[0033] Step 2, extract the resolution and noise level of the SAR image pair from the preprocessed SAR image pair data to preliminarily select the window size (such as 3x3, 5x5, 7x7), and through the test of different window sizes, observe the filtering effect (the window size will affect the filtering effect, the larger window can effectively suppress the noise, but it will lead to the blurring of the edge and details of the SAR image pair; the smaller window is better for retaining details, but the noise suppression ability is relatively weak), and select the optimal window size;

[0034] Step 3, traverse each pixel in the preprocessed SAR image pair data , calculate the average value of all pixel values within the optimal window size centered on the pixel and the variance , and then obtain the equivalent number of views through statistical analysis of the uniform area in the preprocessed SAR image pair data , estimate the noise variance according to the average value of the pixel value using the equivalent number of views (ENL) method

[0035] Step 4, calculate the filtering coefficient by the variance of the pixel value and the noise variance , when , set ; when , set ;

[0036] Calculate the filtered pixel value by the pixel , the filtering coefficient and the average value of the pixel value , and combine the filtered pixel value according to the arrangement mode in the preprocessed SAR image pair data to form the SAR image pair data with noise processed;

[0037] S1.2, check the geographical range of the high-resolution optical image data, ensure that the geographical range of the high-resolution optical image data is consistent with the geographical range of the SAR image pair data processed for noise, obtain the checked optical image data, and then pre-process the checked optical image data (such as resampling, interpolation or cropping) to make it consistent with the resolution and coverage of the SAR image pair data processed for noise, thereby obtaining pre-processed optical image data, and obtaining the imaging parameters in the SAR image pair data processed for noise, the imaging parameters of the SAR image pair including slant range, azimuth angle, incidence angle, and orbit information, then according to the range-doppler model, a mapping relationship between the slant range coordinates of each pixel in the SAR image pair processed for noise and the geographical coordinates in the pre-processed optical image data is established, the terrain height of each pixel is calculated using the pre-processed optical image data, the position of the target monitoring coastal area in the SAR image pair processed for noise is determined combining the imaging parameters of the SAR image pair, each pixel in the SAR image pair processed for noise is converted from the slant range coordinate system to the geographical coordinate system (such as UTM or latitude and longitude), and the converted SAR image pair data is obtained, and then the converted SAR image pair data is checked to ensure that the position and shape of the target monitoring coastal area meet the characteristics of the geographical coordinate system;

[0038] The resolution in the pre-processed optical image data is extracted, and resampling is performed to obtain resampled optical image data for checking to ensure that the geometric accuracy and image quality of the resampled optical image meet the set geometric accuracy threshold and image quality threshold, the imaging parameters in the pre-processed optical image data are extracted, the shadow area in the image is detected according to the imaging parameters of the optical image and the imaging parameters of the SAR image pair, and the occlusion area in the image is identified, then an interpolation method (such as linear interpolation or Kriging interpolation) is used to fill the shadow area, and an interpolation or extrapolation method is used to process the occlusion area to recover the information of the target monitoring coastal area covered by the occlusion area, thereby eliminating or compensating for the influence of the shadow and the occlusion area caused by the terrain undulation, reducing the geometric distortion influence of the terrain undulation on the SAR image, and finally obtaining the processed SAR image pair data;

[0039] S2, obtain the processed image pair data, and perform conjugate multiplication as the main image and the auxiliary image to generate the interferogram, and form the deformation map and judge the deformation area, wherein: the high-pixel image data is used as the main image data, and the low-pixel image data is used as the auxiliary image data; based on the window size of the deformation area, the pixels and the number of pixels of the main image and the auxiliary image are extracted, the coherence coefficient of the deformation area is calculated to judge the candidate reclamation change suspicious area within the set time range value; the main image and the auxiliary image pixels of the candidate reclamation change suspicious area are extracted to capture the change of the ground texture, and the reclamation material of the candidate reclamation change suspicious area is identified;

[0040] S2 includes the following method steps:

[0041] S2.1. The processed SAR image pairs are used as the primary and secondary images, respectively. The high-resolution image data is used as the primary image data, and the low-resolution image data is used as the secondary image data. The secondary image is then precisely aligned with the primary image to ensure pixel-level matching (error less than 0.1 pixels). This yields the registered two image data sets, which are then conjugate-multiplied to generate an interferogram (containing phase and amplitude information). The phase of the processed SAR image pairs is recorded. and To calculate the phase difference of the interferogram ;

[0042] Radar wavelength is obtained from processed SAR imagery. Vertical baseline (Vertical distance between satellite orbits when two SAR images were acquired), slant range (Distance from SAR sensor to surface target), angle of incidence (Angle between SAR beam and surface normal), terrain height (The height of the surface target relative to the reference ellipsoid), then calculate the terrain phase. Using the phase difference of the interferogram Subtract terrain phase The phase difference after removing the terrain phase is obtained. ;

[0043] Atmospheric phase is estimated from SAR imagery. Using the phase difference after removing the terrain phase, subtract the atmospheric phase. Thus, the differential phase is obtained. Differential phase Convert to surface deformation The unit of deformation is meters. This refers to radar wavelength, measured in meters. It is a mathematical constant.

[0044] The surface deformation is stored as a matrix of the same size as the processed SAR image data. The value of each pixel represents the surface deformation of the target monitored coastal area, thus forming a deformation matrix. The deformation matrix is ​​visualized as an image, which in turn forms a deformation map. The surface deformation is used to determine whether there is a deformation area in the deformation map. When the surface deformation is greater than 0, there is obvious surface uplift in the area of ​​the deformation map, and the deformation area is determined to exist in the deformation map. When the surface deformation is less than 0, there is obvious surface subsidence in the area of ​​the deformation map, and the deformation area is determined to exist in the deformation map. If there is a deformation area in the deformation map, it is determined that the spatial range of the reclamation activity has changed.

[0045] S2.2, when the spatial range of reclamation activities changes, obtaining the i-th pixel in the primary image and the secondary image in the deformation area from the processed SAR image pair data and , and then extracting the pixel number from the optimal window in the deformation area , calculating the coherence coefficient of the deformation area through the i-th pixel in the primary image and the secondary image in the deformation area and , the pixel number , wherein * means complex conjugate, and then setting the time range value and the coherence coefficient threshold of the deformation area, using the coherence coefficient of the deformation area and the coherence coefficient threshold of the deformation area to determine whether the deformation area is a candidate reclamation change suspicious area within the set time range value, recording the number of times the coherence coefficient of the deformation area is less than the coherence coefficient threshold of the deformation area and the number of times the coherence coefficient of the deformation area is greater than the coherence coefficient threshold of the deformation area, and when the less number is higher than the greater number, the deformation area exists sharp decline within the set time range value, and the deformation area is a candidate reclamation change suspicious area, otherwise, it is not a candidate reclamation change suspicious area;

[0046] Obtaining the primary image and secondary image pixels of the processed SAR image pair data corresponding to the candidate reclamation change suspicious area, the pixels in the preprocessed optical image data, and performing targeted analysis to capture the changes in surface texture (texture structure change, texture roughness change, texture direction change), identifying the uneven fill distribution range and fill material of the candidate reclamation change suspicious area according to the changes in surface texture, fusing the primary image and secondary image pixels of the processed SAR image pair data and the pixels in the preprocessed optical image data to obtain a fused image, comprehensively using the penetration of SAR image and the hyperspectral information of optical image, using image segmentation algorithm such as region growing-based segmentation method to segment the fused image, identifying the fill area according to the segmentation result (fill area contour, fill area internal feature), and combining the texture change information to identify the fill area and determine the uneven fill distribution range of the candidate reclamation change suspicious area;

[0047] Using the hyperspectral data in the fused image to extract the spectral curve of the fill area, different fill materials have different spectral reflectance characteristics, for example, the reflectivity of sandstone material is higher in the near-infrared band, while the reflectivity of clay material is relatively lower in the visible light band, comparing the extracted spectral curve with the known spectral library of fill materials, and determining the fill material type through spectral matching algorithm (such as spectral angle mapping, minimum distance classification).

[0048] ​S3, the corresponding area of the candidate reclamation change suspicious area in the interferogram is obtained and intercepted, morphological opening operation is performed on the intercepted interferogram candidate suspicious area, the reclamation change suspicious area is identified, and the reclamation change suspicious area is filled according to the type of filling material;

[0049] S3 includes the following method steps:

[0050] S3, the area of the candidate reclamation change suspicious area is calculated according to the uneven filling distribution range of the candidate reclamation change suspicious area, the boundary of the filling distribution range is drawn using a polygon tool (GIS software), the drawn polygon is recorded, the area of the candidate reclamation change suspicious area is directly calculated using an attribute table, the corresponding area of the interferogram is found according to the candidate reclamation change suspicious area, the candidate suspicious area of the interferogram is intercepted, the candidate suspicious area of the interferogram is converted into a binary image, the binary image of the candidate suspicious area is extracted, morphological opening operation is performed on the binary image of the candidate suspicious area, first erosion and then inflation, erosion operation: remove the boundary pixels of the binary image of the candidate suspicious area, reduce the area of the binary image of the candidate suspicious area, and eliminate small spots of the binary image of the candidate suspicious area; inflation operation: restore the boundary of the eroded area in the binary image of the candidate suspicious area, thereby forming a connected region, then an area threshold is set, the area of each connected region is calculated, the connected region with an area less than the area threshold is removed, only the connected region with an area greater than the area threshold is retained, the larger connected region is retained, and finally the connected region output is the reclamation change suspicious area, and then the reclamation change suspicious area is filled according to the type of filling material and the area of the candidate reclamation change suspicious area.

[0051] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring reclamation of a coastal area by satellite remote sensing, S1, acquiring and processing image pair data of different viewing angles of a target monitoring coastal area at consecutive time sequences, characterized in that, Further comprising the following method steps: S2, obtaining the processed image pair data, and performing conjugate multiplication as the main image and the auxiliary image to generate an interferogram, while forming a deformation map and judging a deformation area, wherein: the image data with high pixels is taken as the main image data, and the image data with low pixels is taken as the auxiliary image data; Based on the window size of the deformation area, the pixels and the number of pixels of the main image and the auxiliary image are extracted, the coherence coefficient of the deformation area is calculated to judge the candidate reclamation change suspicious area within the set time range value; The main image and the auxiliary image pixels of the candidate reclamation change suspicious area are extracted to capture the change of the ground texture, and the filling material of the candidate reclamation change suspicious area is identified; In S2.1 of S2, a deformation map is formed by the ground deformation, and the deformation area is automatically distinguished by the deformation map, so as to realize the following method steps for judging the change of the spatial range of the reclamation activity, and realize the following method steps: The atmospheric phase is estimated by the processed SAR image pair data, the phase difference after removing the terrain phase is subtracted by the atmospheric phase, the differential phase is obtained and converted into a ground deformation, and the ground deformation is stored as a matrix with the same size as the processed SAR image pair data to form a deformation matrix and visualize it as an image, thereby forming a deformation map; The ground deformation is used to judge whether there is a deformation area in the deformation map; when the ground deformation is greater than 0, the area in the deformation map has obvious ground uplift, and it is judged that there is a deformation area in the deformation map; when the ground deformation is less than 0, the area in the deformation map has obvious ground subsidence, and it is judged that there is a deformation area in the deformation map; If there is a deformation area in the deformation map, it is judged that the spatial range of the reclamation activity has changed; In S2.2 of S2, the following method steps are realized for automatically distinguishing the candidate reclamation change suspicious area, and the following method steps are realized: The pixels in the main image and the auxiliary image in the deformation area of the processed SAR image pair data are obtained, and the number of pixels is extracted from the window size of the deformation area, and the coherence coefficient of the deformation area is calculated by the pixels in the main image and the auxiliary image in the deformation area and the number of pixels; The time range value and the coherence coefficient threshold of the deformation area are set, and the coherence coefficient of the deformation area is used to judge whether the deformation area is a candidate reclamation change suspicious area within the set time range value; The number of times that the coherence coefficient of the deformation area is less than the coherence coefficient threshold of the deformation area, and the number of times that the coherence coefficient of the deformation area is greater than the coherence coefficient threshold of the deformation area are recorded, and when the less number of times is higher than the greater number of times, it is judged that the deformation area has a sharp decline within the set time range value, and the deformation area is a candidate reclamation change suspicious area; S3, obtaining the corresponding area of the candidate reclamation change suspicious area in the interferogram and intercepting, performing a morphological opening operation on the candidate suspicious area of the intercepted interferogram, identifying the reclamation change suspicious area, and filling the reclamation change suspicious area according to the type of filling material. ​ 2. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 1, characterized in that: The S1, which realizes the speckle noise suppression processing of SAR image data and eliminates the influence of shadow and overlap area caused by terrain undulation on the geometric distortion of SAR image, includes the following method steps and realizes the following steps: The S1.1 in the S1 acquires a pair of SAR image data of target monitoring coastal area at different viewing angles and continuous time sequence by satellite remote sensing technology, and high-resolution optical image data matching the time window of SAR image data, so as to extract SAR image pair data and perform precise orbit data updating, radiation calibration processing, and speckle noise suppression processing of the radiation calibrated SAR image pair data by Lee filtering.

3. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 2, characterized in that: The S1.1 in the S1 utilizes Lee filtering to perform speckle noise suppression processing, including the following method steps: Step 1: Collect the radiation calibrated SAR image pair data, and check whether there are missing values or abnormal values in the basic information of the radiation calibrated SAR image pair data. If there are, perform preprocessing by interpolation method and statistical method; Step 2: Extract the resolution and noise level of the SAR image pair from the preprocessed SAR image pair data, and preliminarily select the window size; Step 3: Traverse the pixels in the preprocessed SAR image pair data, calculate the average value and variance of the pixel values in the window size according to the pixels, and then estimate the noise variance according to the average value of the pixel values by using the equivalent view number method; Step IV, calculating filter coefficient by variance of pixel value and noise variance calculating filter coefficient and calculating filtered pixel value, combining according to arrangement of pre-processed SAR image pair data to form SAR image pair data with processed noise.

4. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 2, characterized in that: The S1.2 in the S1 performs resampling and interpolation preprocessing on the high-resolution optical image data, and at the same time, acquires the imaging parameters in the SAR image pair data processed for noise, including slant range, azimuth angle, incidence angle, and orbit information.

5. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 4, characterized in that: The S1.2 in the S1 realizes the elimination of the influence of shadow and overlap area caused by terrain undulation on the geometric distortion of SAR image as follows, and realizes the following steps: Extract the resolution of the preprocessed optical image data, and perform resampling to check that the geometric accuracy and image quality of the resampled optical image meet the set geometric accuracy threshold and image quality threshold; Extract the imaging parameters of the preprocessed optical image data, detect the shadow area in the image according to the imaging parameters of the optical image and the imaging parameters of the SAR image pair, and identify the overlap area in the image; Fill the shadow area using the interpolation method, and then process the overlap area using the extrapolation method to recover the information of the target monitoring coastal area covered by the overlap area, so as to eliminate the influence of shadow and overlap area caused by terrain undulation, and obtain the processed SAR image pair data.

6. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 5, characterized in that: The S2, which discriminates the candidate reclamation change suspicious area by generating an interference graph, includes the following method steps: The S2.1 in the S2 generates an interference graph to realize the removal of phase difference after terrain phase, including the following method steps and realizing the following method steps: The processed SAR image pair data is taken as the main image and the auxiliary image respectively, the pixels of the auxiliary image are aligned with the main image, the pixel-level matching is ensured, and thus two image data after matching and registration are obtained, and the conjugate multiplication is performed to generate an interferogram, and the phase difference of the interferogram is calculated by recording the phase of the processed SAR image pair data; The radar wavelength, vertical baseline, slant range, incidence angle, and terrain height are obtained through the processed SAR image pair data, and the terrain phase is calculated; The phase difference after removing the terrain phase is calculated by using the phase difference of the interferogram and the terrain phase.

7. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 1, characterized in that: The following method steps are used to identify the uneven fill distribution range and fill material of the candidate reclamation change suspicious area in S2.2 of S2, and the following method steps are performed: The pixels of the main image and the auxiliary image of the processed SAR image pair data corresponding to the candidate reclamation change suspicious area, and the pixels in the preprocessed optical image data are fused; The change of the ground texture is captured by performing targeted analysis on the fused image data; The uneven fill distribution range and fill material of the candidate reclamation change suspicious area are identified according to the change of the ground texture.

8. The method for monitoring the reclamation of the coast by using satellite remote sensing according to claim 1, characterized in that: The following method steps are used to identify the reclamation change suspicious area in S3: The area of the candidate reclamation change suspicious area is calculated according to the uneven fill distribution range of the candidate reclamation change suspicious area; The corresponding area in the interferogram is found through the candidate reclamation change suspicious area, the candidate suspicious area of the interferogram is intercepted, the candidate suspicious area of the interferogram is converted into a binary image, and the binary image of the candidate suspicious area is extracted; The connected regions are formed by performing the erosion operation first and then the inflation operation on the binary image of the candidate suspicious area, and the connected regions larger than the area threshold value are retained, and the connected regions are the reclamation change suspicious area; the reclamation change suspicious area is filled according to the fill material type and the area of the candidate reclamation change suspicious area.

Images