Multi-polarization coherent change detection method based on likelihood estimation

Abstract

The present invention provides a method for detecting multi-polarization coherent changes based on likelihood estimation, including: performing focused imaging processing on repeated orbit multi-polarization interferometric synthetic aperture radar echo data to obtain a first image pair; performing interferometric registration on the first image pair to obtain a second image pair; performing interferometric coherence estimation on the second image pair to obtain a coherent change statistic vector; comparing the coherent change statistic vector with a region segmentation threshold to obtain a multi-polarization coherent change statistic, wherein the region segmentation threshold is used to segment a changed region and an unchanged region in the coherent change statistic vector. The detection method disclosed in the present invention has significant advantages such as accurately reflecting target coherent changes, lower false alarm rate, more comprehensive change information, and better detection performance.

Description

Technical Field

[0001] The present disclosure relates to the technical field of synthetic aperture radar, and in particular to a multi-polarization coherent change detection method based on likelihood estimation. Background Art

[0002] Synthetic Aperture Radar (SAR) is a two-dimensional imaging radar that uses the platform movement to synthesize a large antenna in space to achieve high-resolution imaging of the observed scene. Interferometric Synthetic Aperture Radar (InSAR) combines interferometric measurement technology with SAR technology, and uses the difference in target observation angles to extract interferometric phase information to extract the speed of moving targets, surface deformation, small changes in the ground, and surface elevation information. SAR is a coherent imaging system that can not only obtain the amplitude information of the target, but also the phase information of the target. The phase information contains more detailed features of the observed area and can be used to detect potential changes in the observed scene.

[0003] Traditional detection methods only use the single polarization information of the target, which results in serious false alarms in the final detection results and cannot fully reflect the real change information in the scene, greatly affecting the performance of change detection. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the existing technical problems, the present disclosure provides a multi-polarization coherent change detection method based on likelihood estimation, which is used to at least partially solve the above technical problems.

[0006] (II) Technical solution

[0007] The present invention provides a method for detecting multi-polarization coherent changes based on likelihood estimation, comprising: performing focused imaging processing on repeated orbit multi-polarization interferometric synthetic aperture radar echo data to obtain a first image pair; performing interferometric registration on the first image pair to obtain a second image pair; performing interferometric coherence estimation on the second image pair to obtain a coherent change statistic vector; and comparing the coherent change statistic vector with a region segmentation threshold to obtain a multi-polarization coherent change statistic, wherein the region segmentation threshold is used to segment a changed region and an unchanged region in the coherent change statistic vector.

[0008] Optionally, performing focused imaging processing on the repeated-orbit multi-polarization interference synthetic aperture radar echo data to obtain the first image pair includes: using external digital elevation model data and positioning attitude system data to perform motion compensation and Doppler filtering on the repeated-orbit multi-polarization interference synthetic aperture radar echo data to obtain the first image pair.

[0009] Optionally, the motion compensation includes platform error compensation and residual error compensation.

[0010] Optionally, the first image pair includes a first channel image and a second channel image, and performing Doppler filtering on the repeated orbit multi-polarization interference synthetic aperture radar echo data includes: estimating the Doppler center frequencies of the first channel image and the second channel image respectively to obtain a first Doppler center frequency and a second Doppler center frequency; obtaining the same Doppler center frequency based on the first Doppler center frequency and the second Doppler center frequency; and performing Doppler filtering on the repeated orbit multi-polarization interference synthetic aperture radar echo data using the same Doppler center frequency.

[0011] Optionally, the first image pair includes a first channel image and a second channel image, and interferometric registration of the first image pair includes: coarsely aligning the offsets of the first channel image and the second channel image; searching for same-name points in the first channel image and the second channel image respectively according to a maximum coherence criterion; using a second-order or third-order polynomial to establish a mapping relationship between the offset and the coordinates; and interferometric registration of the first image pair according to the same-name points and the mapping relationship.

[0012] Optionally, performing interference registration on the first image pair further includes: resampling the second channel image; and performing multiple iterative registrations according to the interference registration results of the resampled second channel image and the first image pair.

[0013] Optionally, performing interference coherence estimation on the second image pair to obtain a coherence change statistic vector includes: estimating the coherence estimation value of the second image pair under each polarization condition, wherein the polarization conditions include transmitting horizontal polarization, transmitting vertical polarization, receiving horizontal polarization and receiving vertical polarization; and constructing a coherence change statistic vector according to each coherence estimation value.

[0014] Optionally, performing interference coherence estimation on the second image pair to obtain a coherence variation statistic vector further includes: respectively acquiring coherence variation statistic vectors between channels in the first channel image and the second channel image; judging a scattering mechanism of a target in the image according to the coherence variation statistic vectors between channels; and classifying the target according to the scattering mechanism.

[0015] Optionally, the multi-polarization coherent change detection method based on likelihood estimation further includes: performing radiation correction and radiation calibration on the first image pair.

[0016] Optionally, the accuracy of the interference registration is greater than or equal to 1 / 8 pixel.

[0017] (III) Beneficial effects

[0018] The present disclosure provides a multi-polarization coherent change detection method based on likelihood estimation. In the multi-polarization observation mode of the SAR system, by acquiring repeated orbit multi-polarization interferometric SAR data of the observation scene, and performing interferometric registration and coherence estimation operations on the focused multi-polarization SAR image pair, a coherent change statistic vector that can more comprehensively reflect the change information of the target area is constructed, which can reduce the missed alarm rate of the target area to a certain extent. Finally, according to the principle of likelihood estimation, the final multi-polarization coherent change statistic is obtained. Compared with the coherent change statistic of a single polarization, polarization scattering information is added, which can more accurately characterize the coherent change information of the target. Therefore, compared with the traditional single polarization coherent change detection method, the present disclosure has the advantages of lower missed alarm rate, more comprehensive change information, better detection performance, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0020] Figure 1 The flowchart of the method for detecting multi-polarization coherent changes based on likelihood estimation according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 2 The flowchart of the method for detecting multi-polarization coherent changes based on likelihood estimation according to a specific embodiment of the present disclosure is schematically shown;

[0022] Figure 3 Schematically shows a heavy track interferometry diagram according to an embodiment of the present disclosure;

[0023] Figure 4 The heavy track non-parallel track observation diagram according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0025] It should be noted that in the drawings or descriptions, similar or identical parts use the same figure numbers. The technical features in the various embodiments exemplified in the specification can be freely combined to form a new solution without conflict. In addition, each claim can be used as an embodiment alone or the technical features in each claim can be combined as a new embodiment. In the drawings, the shape or thickness of the embodiment can be expanded and simplified or conveniently indicated. Furthermore, the elements or implementations not shown or described in the drawings are in a form known to a person of ordinary skill in the art. In addition, although demonstrations of parameters containing specific values ​​may be provided herein, it should be understood that the parameters do not need to be exactly equal to the corresponding values, but can be approximated to the corresponding values ​​within an acceptable error tolerance or design constraint.

[0026] Unless there are technical obstacles or contradictions, the above-mentioned various embodiments of the present disclosure can be freely combined to form additional embodiments, and these additional embodiments are all within the protection scope of the present disclosure.

[0027] Although the present disclosure is described in conjunction with the accompanying drawings, the embodiments disclosed in the accompanying drawings are intended to exemplify the preferred embodiments of the present disclosure and should not be construed as limiting the present disclosure. The dimensional ratios in the accompanying drawings are merely illustrative and should not be construed as limiting the present disclosure.

[0028] Although some embodiments of the present general inventive concept have been shown and described, it will be appreciated by those skilled in the art that changes may be made to these embodiments without departing from the principles and spirit of the present general inventive concept, the scope of which is defined by the claims and their equivalents.

[0029] Figure 1 A flow chart of a method for detecting multi-polarization coherent changes based on likelihood estimation according to an embodiment of the present disclosure is schematically shown.

[0030] According to the embodiments of the present disclosure, Figure 1 As shown, the multi-polarization coherent change detection method based on likelihood estimation includes, for example:

[0031] S110, performing focusing imaging processing on the repeated-orbit multi-polarization interferometric synthetic aperture radar echo data to obtain a first image pair.

[0032] Figure 2 A flowchart of a method for detecting multi-polarization coherent changes based on likelihood estimation according to a specific embodiment of the present disclosure is schematically shown.

[0033] According to the embodiments of the present disclosure, Figure 2As shown, for example, the multi-polarization interferometric SAR echo data of repeated orbits are used as the data to be processed. Interferometric SAR takes different images of the same target at different times in the same orbit as images of different channels. Since repeated orbit interferometric SAR adopts repeated orbit acquisition mode, it is difficult to fly in completely overlapping orbits when the platform navigation system is not accurate, especially for airborne platforms, which will bring serious time-varying baseline problems.

[0034] Figure 3 A heavy-track interferometry diagram according to an embodiment of the present disclosure is schematically shown.

[0035] According to the embodiments of the present disclosure, Figure 3 As shown, M and S are the spatial positions of the antennas for the two repeated orbital flights, B is the distance between the two flight antennas, i.e., the spatial baseline, α is the baseline inclination, h1 is the height of antenna M from the ground, and the closest slant distances from the target at point P on the ground to antennas M and S are r1 and r2, respectively. The signals s1 and s2 received by antennas M and S from the target at point P are:

[0036]

[0037]

[0038] in,

[0039]

[0040] m represents the amplitude part of the target signal, j represents the imaginary unit, Indicates the target signal phase of channel 1, Indicates the target signal phase of channel 2, represents the random phase in channel 1 caused by the scattering characteristics, represents the random phase in channel 2 caused by the scattering characteristics, and λ represents the wavelength of the radar transmit signal.

[0041] According to an embodiment of the present disclosure, as shown in formula (3) or formula (4), the signal phase It is mainly composed of two parts: the phase caused by the propagation path and the random phase caused by the scattering characteristics. Different phase parts correspond to different applications. For the target area that has not changed, the scattering characteristics of the same target in two observations are not much different, and the random phases corresponding to the scattering characteristics are also very close. The interference phase can be obtained by conjugating the signals obtained twice, which can be used to accurately invert the target elevation. For the target area that has changed, the scattering characteristics of the same target in two observations are quite different, and the corresponding random phases are also quite different. This will result in the interference phase obtained by conjugating the signals obtained twice. It not only contains the phase part caused by the propagation path difference, but also contains the phase part corresponding to the change in scattering characteristics. This part of information can be used to detect the potential changes in the target area.

[0042] According to the embodiments of the present disclosure, Figure 2 As shown in the figure, in order to obtain a high-quality interferometric image pair, it is necessary to use precise imaging processing to image the two channels separately to obtain a focused SAR image pair to reduce the phase error as much as possible. First, it is necessary to perform focused imaging processing on the repeated orbit multi-polarization interferometric SAR echo data of the observation scene. For example, the positioning and orientation system (POS) data and the external digital elevation model (DEM) data are used to perform motion compensation and Doppler filtering on the repeated orbit multi-polarization interferometric SAR echo data.

[0043] According to the embodiments of the present disclosure, in the re-orbit acquisition mode, since the platform is affected by the atmospheric airflow during the two flights, the attitude of the platform will also change accordingly, especially for the airborne platform, which will cause the SAR image pairs acquired during the two flights to have two-dimensional space-variant characteristics. In order to make the interferometric SAR image pairs have a smaller phase error, it is necessary to perform accurate motion compensation processing and sub-aperture motion compensation processing on them. Motion compensation processing is, for example, error compensation for the two motion trajectories of the SAR to reduce the measurement error caused by the inconsistency of the SAR's running routes. Sub-aperture motion compensation is, for example, residual error compensation, that is, reducing the measurement error caused by insufficient measurement accuracy of the SAR.

[0044] According to the embodiments of the present disclosure, since the motion of the platform causes its attitude to change at all times, for the repeated orbit acquisition mode, there is a large difference in the attitude of the two times, which will also cause the interferometric SAR image pairs acquired in the two flights to have different Doppler center frequencies. In order to reduce the impact of Doppler decorrelation on the interference results, it is necessary to perform Doppler filtering on the repeated orbit interferometric SAR image pairs, that is, to estimate the Doppler center of the SAR image pairs acquired twice in the repeated orbit respectively, and then take the same Doppler center frequency for imaging, so as to minimize the coherence loss caused by the processing, for example

[0045] f dc =min(f dc1 , f dc2 ) (5)

[0046] Among them, f dc represents the same Doppler center frequency, f dc1 Indicates the Doppler center frequency of channel 1 image, f dc2 Represents the Doppler center frequency of channel 2 image. The same Doppler center frequency can take the minimum value of the two, as shown in formula (5), or the maximum value of the two, or the average value of the two, as long as the two are unified to the same Doppler center frequency.

[0047] According to the embodiments of the present disclosure, Figure 2 As shown in the figure, due to the unstable flight attitude of the SAR system and the instability of the system when collecting data, the amplitude difference of the observation area before and after the change is large. In order to reduce the impact of the two acquisitions of SAR images on the amplitude imbalance, it is necessary to perform radiation correction and radiation calibration on the SAR image pair after focused imaging processing according to the antenna radiation pattern of the SAR system and the real-time position and attitude information of the platform recorded by POS during data collection.

[0048] S120, performing interference registration on the first image pair to obtain a second image pair.

[0049] Figure 4 The heavy track non-parallel track observation diagram according to an embodiment of the present disclosure is schematically shown.

[0050] According to the embodiments of the present disclosure, Figure 4 As shown, Flight1, for example, represents the shooting track (track) where the main image is located in the heavy track, and Flight2, for example, represents the shooting track where the secondary image (or auxiliary image) is located in the heavy track. Due to the influence of the non-parallel tracks and residual motion errors, the two SAR images observed in the two heavy tracks still rotate after focusing imaging, that is, there is still a position offset in the azimuth and range directions. Due to the difference in the two imaging planes, the target point P has the following relationship in the two imaging coordinate systems (y, r) and (y′, r′):

[0051]

[0052] Among them, y and r represent the position of the target in azimuth and range respectively, y0 and r0 represent the offset in azimuth and range respectively, and θ represents the rotation angle caused by the non-parallel track. Therefore, after obtaining the interferometric SAR image pair after focused imaging, it is necessary to use, for example, the maximum coherence coefficient method to perform interferometric registration processing on the multi-polarization interferometric SAR image pair, or continue to resample the auxiliary SAR image, iterate the registration multiple times, and output the registered interferometric result.

[0053] Preferably, the interference registration accuracy reaches, for example, 1 / 8 pixel.

[0054] According to the embodiments of the present disclosure, Figure 2 As shown in the figure, since the residual motion errors of the interferometric SAR images acquired by the re-orbit are independent and difficult to estimate, the offsets of the main and auxiliary images in azimuth and distance are random, which makes it difficult to achieve high-precision registration through traditional registration methods. In order to achieve a higher-precision registration result that meets the needs of interference, it is necessary to improve the traditional registration method based on homonymous points. First, the offsets of the main and auxiliary images estimated according to the orbital information and geometric relationship are roughly registered (i.e., 1-pixel level registration), and then the homonymous points such as sub-pixel level are searched in the field according to the maximum coherence criterion, and high-quality homonymous points are screened out, and then the mapping relationship between the offset and the coordinate is established, usually using a second-order or third-order polynomial. For general interferometric applications, the above processing can meet the application requirements. However, for small change detection, the registration results obtained at this time cannot be directly used for detection, and further improvement is needed. The present disclosure applies the iterative concept to the registration process, and performs multiple iterations in the fine registration part. For example, after obtaining the first registration result, the auxiliary SAR image is resampled, and then the first registered image and the resampled image are re-registered to obtain the second registered image, and then the auxiliary SAR image is resampled, and then the second registered image and the resampled image are re-registered, and so on, and the registration is continuously iterated until the required measurement accuracy is achieved. In this way, the error of registration using the above registration method for general interference applications can be reduced to a certain extent, and more satisfactory results can be obtained.

[0055] S130, performing interference coherence estimation on the second image pair to obtain a coherence variation statistic vector.

[0056] According to the embodiments of the present disclosure, Figure 2As shown in the figure, in order to characterize the coherent change information of the observed scene, the interferometric coherence estimation can be performed on the SAR image pair after interferometric registration processing to obtain the coherent change statistics of each single polarization of the observed scene, forming a coherent change statistic vector. Interferometric coherence characterizes the degree of change of the target. Under multi-polarization observation, the decoherence degree of the target under different polarization scattering mechanisms is different, which leads to differences in the complex coherence coefficients under different polarization states. After obtaining the registered SAR image pair, in order to obtain statistics that more comprehensively characterize the change information, first, the coherence estimation value under each polarization scattering mechanism observation is estimated separately:

[0057]

[0058] Among them, s1 and s2 are the signals received by antennas M and S at the target at point P, respectively. * is the conjugate of s2. The polarization scattering mechanism includes, for example, horizontal polarization or vertical polarization of the transmitted signal and horizontal polarization or vertical polarization of the received signal, which is called multi-polarization scattering mechanism. Formula (7) is used to obtain the interference coherence coefficient of different polarization channels of the observation scene, and then the coherence change statistic vector is constructed, which is expressed as:

[0059] γ c =[γ HH , γ VV , γ HV ] (8)

[0060] Among them, γ HH is the interference coherence coefficient of the HH channel (horizontally polarized channel of the transmitting signal and horizontally polarized channel of the receiving signal), γ VV is the interference coherence coefficient of the VV channel (the vertical polarization channel of the transmitting signal and the vertical polarization channel of the receiving signal), γ HV is the interference coherence coefficient of the HV channel (horizontally polarized channel of the transmitting signal and vertically polarized channel of the receiving signal).

[0061] S140, comparing the coherent change statistic vector with the region segmentation threshold to obtain a multi-polarization coherent change statistic, wherein the region segmentation threshold is used to segment a changed region from an unchanged region in the coherent change statistic vector.

[0062] According to the embodiments of the present disclosure, Figure 2As shown, according to the coherent change statistics, the changed area shows low coherence characteristics and the unchanged area shows high coherence characteristics. The polarization information obtained by the SAR system, that is, the multi-polarization SAR image pair can more comprehensively reflect the polarization scattering characteristics of the target from different angles. By combining the coherent change information under different polarization states, the real change information of the observation area can be more accurately displayed. The above coherent change statistic vector contains the coherent change information of the target under full polarization observation, which can be used to obtain better detection effect than single polarization. Coherent change detection usually divides the changed area from the unchanged area according to the coherence before and after the observation scene changes. Since the target has different scattering characteristics under different polarization scattering mechanisms, this will lead to different decoherence degrees in the changed target area. According to the principle of coherent change detection, the likelihood estimation of the coherent change statistic vector can obtain a multi-polarization coherent change statistic that can more comprehensively characterize the changes in the observation scene, that is:

[0063]

[0064] Among them, Th v It is the regional segmentation threshold used to segment the changed area and the unchanged area in the registered multi-polarization SAR image pair. That is, by judging the coherent change statistic vector value of each pixel in the registered multi-polarization SAR image pair and the size of the threshold, each pixel is divided into the changed area or the unchanged area. v For example, it can be determined by the constant false alarm rate detection (CFAR) method. In this way, the relationship between the multi-polarization coherent change statistic and the coherent change statistic vector is established, and the final change detection statistic can be obtained according to formula (9), which can reduce the false alarm rate of detection and improve the performance of change detection.

[0065] According to the embodiments of the present disclosure, the polarization coherence information of the target is composed of two parts: the polarization coherence information between SAR images before and after the change and the polarization coherence information between channels in the SAR image. The former reflects the coherence change of the target under different polarization states, which can reduce the false alarm rate and improve the performance of change detection compared with single polarization observation. The latter reflects the scattering information of the target in different polarization modes, which can be used to determine which scattering mechanism dominates the target, determine the structural type of the target, and classify the target according to different dominant scattering modes, thereby eliminating the uninteresting change type from the detection results, or eliminating false alarm interference caused by signal-to-noise ratio areas such as overlap and shadow.

[0066] In summary, the embodiments of the present disclosure propose a method for detecting multi-polarization coherent changes based on likelihood estimation. Through precise motion compensation and Doppler filtering to process repeated orbit multi-polarization interferometric SAR echo data, focused imaging of repeated orbit interferometric SAR image pairs is achieved, and then the repeated orbit interferometric SAR image pairs are subjected to interferometric registration, coherence estimation, and likelihood estimation, etc., which increases the observed amount of polarization information of the target area and realizes a detailed characterization of the change information of the observed scene. The detection method disclosed in the present disclosure has significant advantages such as accurate reflection of target coherent changes, lower false alarm rate, more comprehensive change information, and better detection performance.

[0067] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to a specific order or hierarchy.

[0068] It should also be noted that the directional terms mentioned in the embodiments, such as "upper", "lower", "front", "back", "left", "right", etc., are only reference directions of the drawings and are not intended to limit the scope of protection of the present disclosure. Throughout the drawings, the same elements are represented by the same or similar reference numerals. Conventional structures or configurations will be omitted when they may cause confusion in the understanding of the present disclosure. In addition, the shapes, sizes, and positional relationships of the components in the drawings do not reflect the actual sizes, proportions, and actual positional relationships.

[0069] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the attached claims, the disclosure is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the attached claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

[0070] In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. With respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", as explained in terms of "including," used as a transitional word in the claims. Any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0071] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present disclosure. It should be understood that the above description is only a specific embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A multi-polarization coherent change detection method based on likelihood estimation, characterized in that: include: Performing focusing imaging processing on the repeated-orbit multi-polarization interferometric synthetic aperture radar echo data to obtain a first image pair; performing interference registration on the first image pair to obtain a second image pair; performing interference coherence estimation on the second image pair to obtain a coherence variation statistic vector; Comparing the coherent change statistic vector with a region segmentation threshold to obtain a multi-polarization coherent change statistic, wherein the region segmentation threshold is used to segment a changed region and an unchanged region in the coherent change statistic vector; The acquisition of the repeated orbit multi-polarization interferometric synthetic aperture radar echo data is based on the following formula: s1=m(r1)exp(jφ1) s2=m(r2)exp(jφ2) Where s1 is the target signal received by the radar during the first flight, s2 is the target signal received by the radar during the second re-orbit flight, m ​​represents the amplitude part of the corresponding target signal, j represents the imaginary unit, φ1 represents the target signal phase of the first channel, φ2 represents the target signal phase of the second channel, φ1=4πr1 / λ+φ 1_scatt ,φ2=4πr2 / λ+φ 2_scatt , r1 represents the shortest slant distance from the target to the radar during the first flight, r2 represents the shortest slant distance from the target to the radar during the second re-orbit flight, φ 1_scatt represents the random phase caused by the scattering characteristics in the first channel, φ 2_scatt represents the random phase in the second channel caused by the scattering characteristics, and λ represents the wavelength of the transmitted signal of the radar.

2. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 1, characterized in that: The step of performing focusing imaging processing on the repeated orbit multi-polarization interferometric synthetic aperture radar echo data to obtain a first image pair includes: The first image pair is obtained by performing motion compensation and Doppler filtering on the repeated orbit multi-polarization interferometric synthetic aperture radar echo data using external digital elevation model data and positioning and attitude determination system data.

3. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 2, characterized in that: The motion compensation includes platform error compensation and residual error compensation.

4. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 2, characterized in that: The first image pair includes a first channel image and a second channel image, and performing Doppler filtering on the repeated track multi-polarization interferometric synthetic aperture radar echo data includes: respectively estimating the Doppler center frequencies of the first channel image and the second channel image to obtain a first Doppler center frequency and a second Doppler center frequency; Obtaining the same Doppler center frequency according to the first Doppler center frequency and the second Doppler center frequency; Doppler filtering is performed on the repeated-orbit multi-polarization interferometric synthetic aperture radar echo data using the same Doppler center frequency.

5. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 1, characterized in that: The first image pair includes a first channel image and a second channel image, and performing interference registration on the first image pair includes: Performing a rough registration on the offsets of the first channel image and the second channel image; Searching for points of the same name in the first channel image and the second channel image respectively according to a maximum coherence criterion; A second-order or third-order polynomial is used to establish a mapping relationship between the offset and the coordinates; According to the same-name points and the mapping relationship, interference registration is performed on the first image pair.

6. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 5, characterized in that: The interferometric registration of the first image pair further comprises: Resampling the second channel image; Multiple iterative registrations are performed according to the interference registration results of the resampled second channel image and the first image pair.

7. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 1, characterized in that: The performing interference coherence estimation on the second image pair to obtain a coherence variation statistic vector comprises: respectively estimating a coherence estimation value of the second image pair under each polarization condition, wherein the polarization conditions include transmitting horizontal polarization, transmitting vertical polarization, receiving horizontal polarization and receiving vertical polarization; The coherence variation statistic vector is constructed according to each of the coherence estimation values.

8. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 7, characterized in that: The performing interference coherence estimation on the second image pair to obtain a coherence variation statistic vector further comprises: Respectively obtaining coherent variation statistics vectors between channels in the first channel image and the second channel image; Determining the scattering mechanism of the target in the image according to the coherent variation statistic vector between the channels; The targets are classified according to the scattering mechanism.

9. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 1, characterized in that: The multi-polarization coherent change detection method based on likelihood estimation also includes: The first image pair is radiometrically corrected and radiometrically calibrated.

10. The method for detecting multi-polarization coherent changes based on likelihood estimation according to claim 1, characterized in that: The accuracy of the interference registration is greater than or equal to 1 / 8 pixel.

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