InSAR sequence time de-coherence suppression method based on complex covariance matrix
By using a maximum likelihood criterion estimation method based on the complex covariance matrix, the problem of temporal decorrelation in InSAR technology is solved, the coherence of interferograms and measurement accuracy are improved, and the algorithm complexity is simplified.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING INST OF TECH
- Filing Date
- 2025-04-14
- Publication Date
- 2026-06-12
Smart Images
Figure CN120522705B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of synthetic aperture radar technology, and particularly relates to an InSAR sequence temporal decoherence suppression method based on complex covariance matrix. Background Technology
[0002] Synthetic Aperture Radar (SAR) is a high-resolution imaging radar that has become an indispensable and important technical means in the field of Earth observation due to its all-weather, all-time operation capabilities and wide coverage. Synthetic Aperture Radar Interferometry (InSAR), developed based on SAR technology, acquires two SAR images of the same area with spatial or temporal baseline differences, constructs an interferogram containing phase difference information, and then accurately calculates the interferometric phase information to deduce the three-dimensional spatial coordinates and deformation characteristics of the observed target.
[0003] In the process of acquiring InSAR images, if a long time baseline is used, it is easy to cause significant temporal decorrelation. That is, because the surface features change over time, the scattering characteristics of the radar echo signals are inconsistent between the two SAR images, which leads to a significant reduction in the correlation coefficient of the generated interferogram, affecting the imaging quality and measurement accuracy.
[0004] Currently, widely used methods for suppressing temporal decoherence utilize techniques such as permanent scatterers (PS) or distributed scatterers (DS) to select regions with stable surface features for processing, thereby improving the coherence of interferograms. However, the PS method relies on stable scatterers, resulting in insufficient spatial density, especially in non-urban areas where its performance is poor. While the DS method offers wider coverage, it requires high scatterer stability and has complex algorithms. Furthermore, to reduce phase noise generated by temporal decoherence, filtering methods are often used to denoise the interferograms. Current filtering methods mostly process interferograms in the spatial domain, without utilizing temporal information, thus limiting their effectiveness in suppressing temporal decoherence. Some improved multi-temporal filtering methods only process the intensity of the interferogram, failing to preserve its phase information and offering no benefit to subsequent InSAR processing steps. Summary of the Invention
[0005] To address the temporal decoherence suppression problem in SAR interferogram sequences, this invention proposes a temporal decoherence suppression method for InSAR sequences based on the complex covariance matrix. This method can determine homogeneous pixels in the spatiotemporal joint domain based on the statistical characteristics of the complex covariance matrix of scattering units, and estimate the complex covariance matrix after temporal decoherence suppression based on the maximum likelihood criterion, thereby deriving more accurate information on interferogram intensity, phase, and correlation coefficients.
[0006] The technical solution for implementing the present invention is as follows:
[0007] In a first aspect, the present invention provides a temporal decoherence suppression method for InSAR sequences based on the complex covariance matrix, the specific process of which is as follows:
[0008] For the input complex vectors of multiple SLC SAR images, the following processing is performed at each pixel x:
[0009] Extract the p-dimensional complex scattering vector z of a single pixel at pixel x. j (x), and generate the initial complex covariance matrix C(x), where j = 1, 2, ..., p is the complex value of the j-th image at pixel x;
[0010] Generate a rectangular search window W centered at pixel x. x For the input complex vectors of multiple SLC SAR images, the p-dimensional complex scattering vector z of a single pixel is extracted at pixel y within the search window. j (y), generate the reference complex covariance matrix C(y);
[0011] Let pixel y traverse the rectangular search window W x For each pixel except pixel x, calculate the maximum likelihood test statistic Q(x,y) based on the initial complex covariance matrix C(x) and the reference complex covariance matrix C(y);
[0012] The complex covariance matrix estimated based on the maximum likelihood test metric Q(x,y) Then, the intensity, phase, and correlation coefficient of the decoherently suppressed InSAR sequence at pixel x are extracted from it.
[0013] Finally, the temporal decoherence suppression results of the complete SAR interferogram sequence were obtained.
[0014] Optionally, the maximum likelihood test statistic Q(x,y) of this invention is:
[0015]
[0016] Where |·| represents the determinant of the matrix, and n represents the size of the complex correlation calculation window.
[0017] Optionally, the complex covariance matrix estimated based on the maximum likelihood test metric Q(x,y) according to the present invention... The specific process is as follows:
[0018] Calculate the weight w(x,y) based on the maximum likelihood test metric Q(x,y);
[0019] Based on the weight of each pixel within the window, the complex covariance matrix corresponding to pixel x is estimated.
[0020] Optionally, the weight w(x,y) of this invention is:
[0021] w(x,y)=-ln Q(x,y)
[0022] The complex covariance matrix for:
[0023]
[0024] in, This is the normalization coefficient.
[0025] Optionally, the present invention uses the complex covariance matrix The intensity, phase, and correlation coefficient of the decoherent-suppressed InSAR sequence at pixel x are extracted.
[0026]
[0027] in, This represents the intensity of the i-th image at pixel x. This represents the intensity of the j-th image at pixel x. This represents the phase of the interferogram generated from the i-th image and the j-th image at pixel x. The correlation coefficient at pixel x of the interferograms generated from the i-th and j-th images.
[0028] In a second aspect, the present invention provides an InSAR sequence temporal decoherence suppression device based on a complex covariance matrix, comprising: a complex covariance matrix generation module, a likelihood statistics calculation module, and a decorrelation suppression module;
[0029] The complex covariance matrix generation module is used to extract the p-dimensional complex scattering vector z of a single pixel at pixel x, based on the complex vectors of multiple input SLC SAR images. j (x), and generate the initial complex covariance matrix C(x), where j = 1, 2, ..., p are the complex values of the j-th image at pixel x; generate a rectangular search window W centered on pixel x. x For the input complex vectors of multiple SLC SAR images, the p-dimensional complex scattering vector z of a single pixel is extracted at pixel y within the search window. j (y), generate the reference complex covariance matrix C(y);
[0030] The likelihood statistic calculation module is used to calculate the maximum likelihood test statistic Q(x,y) based on the initial complex covariance matrix C(x) and the reference complex covariance matrix C(y).
[0031] The decorrelation suppression module estimates the complex covariance matrix based on the maximum likelihood test econometric Q(x,y). The intensity, phase, and correlation coefficient of the decoherent-suppressed InSAR sequence at pixel x are extracted from it; finally, the temporal decoherent suppression result of the complete SAR interferogram sequence is obtained.
[0032] Beneficial effects:
[0033] The temporal decoherence suppression method mentioned in this invention can determine homogeneous pixels in the spatiotemporal joint domain based on the statistical characteristics of the complex covariance matrix of the scattering unit, making full use of the temporal information of the original SAR sequence; and based on the maximum likelihood criterion, it estimates the complex covariance matrix after time-decoherence suppression, preserving the phase information of the interferogram sequence. Attached Figure Description
[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 This is a flowchart of the steps of the present invention.
[0036] Figure 2 This is the raw, unprocessed InSAR interferometric phase map.
[0037] Figure 3 The image shows the InSAR interferometric phase map obtained after processing using the method described in this invention. Detailed Implementation
[0038] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0039] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0040] It should be noted that various aspects of the embodiments described below are within the scope of the appended claims. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.
[0041] In this embodiment of the application, the user input is a complex vector S = [S1, S2, ..., S] generated from p SLC SAR images. p ], where S1, S2, ..., S p The complex matrix of a single SLC SAR image has a dimension of l. row ×l col , l row and l col These represent the number of rows and columns of the image, respectively. The size of the complex correlation calculation window is n, and the side length of the search window is t. The output is the intensity, phase, and correlation coefficient of the SAR interferometer pair after decoherence suppression. This application provides an embodiment of an InSAR sequence temporal decoherence suppression method based on the complex covariance matrix, with the following specific steps:
[0042] For the input complex vectors of multiple SLC SAR images, the following processing is performed at each pixel x:
[0043] Extract the p-dimensional complex scattering vector z of a single pixel at pixel x. j (x), and generate the initial complex covariance matrix C(x), where j = 1, 2, ..., p is the complex value of the j-th image at pixel x;
[0044] Generate a rectangular search window W centered at pixel x. x For the input complex vectors of multiple SLC SAR images, the p-dimensional complex scattering vector z of a single pixel is extracted at pixel y within the search window. j (y), generate the reference complex covariance matrix C(y);
[0045] Let pixel y traverse the rectangular search window W x For each pixel except pixel x, calculate the maximum likelihood test statistic Q(x,y) based on the initial complex covariance matrix C(x) and the reference complex covariance matrix C(y);
[0046] The complex covariance matrix estimated based on the maximum likelihood test metric Q(x,y) Then, the intensity, phase, and correlation coefficient of the decoherent-suppressed InSAR sequence at pixel x are extracted from it.
[0047] Finally, the temporal decoherence suppression results of the complete SAR interferogram sequence were obtained.
[0048] The above process will be explained in detail below, such as Figure 1 As shown:
[0049] Step 1: Extract the p-dimensional complex scattering vector Z(x) of a single pixel.
[0050] At pixel x, from the input l row ×l col Extracting the p-dimensional complex scattering vector Z(x) of a single pixel from the p-dimensional SLC SAR image complex vector.
[0051] Z(x) = [z1(x), z2(x), ..., z p (x)] T (1)
[0052] Among them, z j (x)(j=1,2,…,p)=S j (x) is the complex value of the j-th image at pixel x.
[0053] Step 2: Generate the initial complex covariance matrix based on the scattering vector Z(x).
[0054] Based on the input complex correlation calculation window size n, generate the initial complex covariance matrix C(x).
[0055]
[0056] Step 3: Generate a rectangular search window
[0057] Generate a rectangular search window W with size t×t centered at pixel x. x .
[0058] Step 4: Extract the p-dimensional complex scattering vector Z(y) of a single pixel from the search window.
[0059] In the search window W x Take a pixel y within the matrix, and extract the complex scattering vector Z(y) following step 1.
[0060] Z(y) = [z1(y), z2(y), ..., z p (y)] T (3)
[0061] Among them, z j (y)(j=1,2,…,p)=S j(y) is the complex value of the j-th image at pixel y.
[0062] Step 5: Generate the reference complex covariance matrix
[0063] Generate a reference complex covariance matrix C(y) at pixel y.
[0064]
[0065] Step 6: Calculate the maximum likelihood test statistic.
[0066] Calculate the maximum likelihood test statistic Q(x,y) based on the initial complex covariance matrix C(x) and the reference complex covariance matrix C(y).
[0067]
[0068] Where |·| represents the determinant of the matrix.
[0069] The derivation of the maximum likelihood test statistic is as follows:
[0070] The complex covariance matrix C(x) follows a complex Wishart distribution, with the following probability density function:
[0071]
[0072] in, Σ(x)=E{Z(x)Z(x) H} is the covariance matrix of pixel x.
[0073] Based on the maximum likelihood derivation using this probability density function, we have the following two assumptions:
[0074] Assume H0: The covariance matrices of the two distributions are the same: Σ(x)=Σ(y)=Σ
[0075] Assumption H1: The covariance matrices of the two distributions are different Σ(x) ≠ Σ(y).
[0076] Under assumption H0, the joint likelihood function is:
[0077] f(C(x),C(y)|n,Σ)=w(C(x)|n,Σ)·w(C(y)|n,Σ) (7)
[0078] The log-likelihood is:
[0079]
[0080] Under hypothesis H0, the maximum likelihood estimate can be obtained as follows:
[0081]
[0082] Similarly, under assumption H1, the maximum estimate can be obtained as:
[0083]
[0084] Therefore, the maximum likelihood ratio is:
[0085]
[0086] Using the maximum likelihood ratio as the maximum likelihood test statistic, we can obtain:
[0087]
[0088] Step 7: Calculate the weights w(x,y) based on the maximum likelihood test statistic Q(x,y).
[0089] w(x,y)=-ln Q(x,y) (13)
[0090] Since a larger value of the maximum likelihood test statistic indicates a higher similarity between two pixels, the weight is defined as the complex logarithm of the maximum likelihood test statistic in order to quantitatively perform weighted averaging based on the degree of similarity.
[0091] Step 8: Weighted averaging achieves decoherence suppression
[0092] In the generated search window, steps 4 through 7 are traversed, weights are calculated pixel by pixel, and a weighted average is performed to achieve decoherence suppression, resulting in the estimated complex covariance matrix. The formula is as follows
[0093]
[0094] in, This is the normalization coefficient.
[0095] Step 9: Extract the intensity, phase, and correlation coefficient of the decoherent suppression InSAR sequence.
[0096] From the complex covariance matrix Extract the intensity, phase, and correlation coefficient of pixel x after decoherence suppression.
[0097]
[0098] in, This represents the intensity of the i-th image at pixel x. This represents the intensity of the j-th image at pixel x. This represents the phase of the interferogram generated from the i-th image and the j-th image at pixel x. The correlation coefficient at pixel x of the interferograms generated from the i-th and j-th images.
[0099] Step 10, iterate through all l row ×l col Repeat steps 1 to 9 for each pixel to obtain the temporal decoherence suppression result of the complete SAR interferogram sequence.
[0100] In a second aspect, the present invention provides an InSAR sequence temporal decoherence suppression device based on a complex covariance matrix, comprising: a complex covariance matrix generation module, a likelihood statistics calculation module, and a decorrelation suppression module;
[0101] The complex covariance matrix generation module is used to extract the p-dimensional complex scattering vector z of a single pixel at pixel x, based on the complex vectors of multiple input SLC SAR images. j (x), and generate the initial complex covariance matrix C(x), where j = 1, 2, ..., p are the complex values of the j-th image at pixel x; generate a rectangular search window W centered on pixel x. x For the input complex vectors of multiple SLC SAR images, the p-dimensional complex scattering vector z of a single pixel is extracted at pixel y within the search window. j (y), generate the reference complex covariance matrix C(y);
[0102] The likelihood statistic calculation module is used to calculate the maximum likelihood test statistic Q(x,y) based on the initial complex covariance matrix C(x) and the reference complex covariance matrix C(y).
[0103] The decorrelation suppression module estimates the complex covariance matrix based on the maximum likelihood test econometric Q(x,y). The intensity, phase, and correlation coefficient of the decoherent-suppressed InSAR sequence at pixel x are extracted from it; finally, the temporal decoherent suppression result of the complete SAR interferogram sequence is obtained.
[0104] The following provides an implementation example with specific parameters.
[0105] In this example, the input data consists of three TerraSAR-X images, covering the Beijing Regent Hotel and some surrounding buildings. The correlation calculation window size is set to n=9, and the search window side length is set to t=9.
[0106] First, steps 1 and 2 generate the initial complex covariance matrix. Then, step 3 generates the search window. Next, steps 4 and 5 generate the reference complex covariance matrix. Then, steps 6 and 7 calculate the maximum likelihood test statistic and further calculate the weights. Finally, steps 8, 9, and 10 complete the temporal decoherence suppression of the SAR interferometric sequence.
[0107] Figure 2The original interference phases of two interference pairs in the interferogram sequence are given. Figure 3 The interference phases after processing by the method proposed in this invention are shown. By comparison, it can be seen that the phase noise of both interference pairs has been significantly reduced, effectively suppressing the temporal decoherence of the original interferogram.
[0108] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A temporal decoherence suppression method for InSAR sequences based on the complex covariance matrix, characterized in that, The specific process is as follows: For the input complex vectors of multiple SLC SAR images, at each pixel The following processing is performed at this location: In pixels Extract a single pixel 3D complex scattering vector And generate the initial complex covariance matrix. ,in, It is the first Image in pixels Complex values at the location; In pixels Generate a rectangular search window centered on the target area. For the input complex vectors of multiple SLC SAR images, the pixels within the search window Extract a single pixel 3D complex scattering vector Generate a reference complex covariance matrix ; Let pixels Traverse the rectangular search window Pixels For each pixel other than the initial complex covariance matrix, and reference complex covariance matrix Calculate the maximum likelihood test statistic ; Based on the maximum likelihood test statistic Estimated complex covariance matrix And extract pixels from them The intensity, phase, and correlation coefficient of the InSAR sequence after decoherence suppression at the location; Finally, the temporal decoherence suppression results of the complete SAR interferogram sequence were obtained; The maximum likelihood test statistic for: in, Represents the determinant of a matrix. This indicates the size of the complex correlation calculation window.
2. The InSAR sequence temporal decoherence suppression method based on the complex covariance matrix according to claim 1, characterized in that, The maximum likelihood test statistic Estimated complex covariance matrix The specific process is as follows: Based on the maximum likelihood test statistic Calculate weights ; Based on the weight of each pixel within the window, the corresponding pixel is estimated. Complex covariance matrix at .
3. The InSAR sequence temporal decoherence suppression method based on the complex covariance matrix according to claim 2, characterized in that, The weight for: The complex covariance matrix for: in, This is the normalization coefficient.
4. The InSAR sequence temporal decoherence suppression method based on the complex covariance matrix according to claim 3, characterized in that, From the complex covariance matrix Extracting pixels Intensity, phase, and correlation coefficient of the InSAR sequence after decoherence suppression. in, Indicates the first Image in pixels Strength at that location, Indicates the first Image in pixels Strength at that location, Indicates the first Image and the first Interferogram generated from a single image at the pixel level Phase at that point, No. Image and the first Interferogram generated from a single image at the pixel level The correlation coefficient at the location.
5. A temporal decoherence suppression device for InSAR sequences based on complex covariance matrix, characterized in that, include: The module includes a complex covariance matrix generation module, a likelihood statistic calculation module, and a decorrelation suppression module. The complex covariance matrix generation module is used to generate the complex vector from multiple input SLC SAR images at the pixel level. Extract a single pixel 3D complex scattering vector And generate the initial complex covariance matrix. ,in, It is the first Image in pixels Complex values at; in pixels Generate a rectangular search window centered on the target area. For the input complex vectors of multiple SLC SAR images, the pixels within the search window Extract a single pixel 3D complex scattering vector Generate a reference complex covariance matrix ; The likelihood statistics calculation module is used to calculate the likelihood based on the initial complex covariance matrix. and reference complex covariance matrix Calculate the maximum likelihood test statistic ; The decorrelation suppression module, based on the maximum likelihood test statistic... Estimated complex covariance matrix And extract pixels from them The intensity, phase, and correlation coefficient of the InSAR sequence after decoherence suppression are obtained; finally, the temporal decoherence suppression results of the complete SAR interferogram sequence are obtained. The maximum likelihood test statistic for: in, Represents the determinant of a matrix. This indicates the size of the complex correlation calculation window.
6. The InSAR sequence temporal decoherence suppression device based on the complex covariance matrix according to claim 5, characterized in that, The maximum likelihood test statistic Estimated complex covariance matrix The specific process is as follows: Based on the maximum likelihood test statistic Calculate weights ; Based on the weight of each pixel within the window, the corresponding pixel is estimated. Complex covariance matrix at .
7. The InSAR sequence temporal decoherence suppression device based on the complex covariance matrix according to claim 6, characterized in that, The weight for: The complex covariance matrix for: in, This is the normalization coefficient.
8. The InSAR sequence temporal decoherence suppression device based on the complex covariance matrix according to claim 7, characterized in that, From the complex covariance matrix Extracting pixels The intensity, phase, and correlation coefficient of the InSAR sequence after decoherence suppression at the point of origin. in, Indicates the first Image in pixels Strength at that location, Indicates the first Image in pixels Strength at that location, Indicates the first Image and the first Interferogram generated from a single image at the pixel level Phase at that point, No. Image and the first Interferogram generated from a single image at the pixel level The correlation coefficient at the location.