CN111665503A - Satellite-borne SAR image data compression method - Google Patents

Satellite-borne SAR image data compression method Download PDF

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CN111665503A
CN111665503A CN202010374453.6A CN202010374453A CN111665503A CN 111665503 A CN111665503 A CN 111665503A CN 202010374453 A CN202010374453 A CN 202010374453A CN 111665503 A CN111665503 A CN 111665503A
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左小清
李勇发
黄亮
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Kunming University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses a compression method of satellite-borne SAR image data, belonging to the field of synthetic aperture radar interferometry (InSAR) data processing and analysis, comprising the following steps: dividing the registered original image into several small stacks, and generating a differential interference image in each stack; constructing a Phase estimator, and performing Phase-Linking in each micro stack; constructing a data compression model, and compressing SAR image data in each micro stack to obtain a virtual image; performing interference processing by using the virtual image obtained in each micro stack to generate an artificial interference pattern; and performing time sequence InSAR processing and analysis by using the generated artificial interferogram. The invention introduces a data compression method in the time sequence InSAR processing process, can greatly reduce the data processing amount and the storage amount under the condition of ensuring the precision, improves the SAR data processing efficiency, lays a foundation for processing InSAR big data, and makes the real-time monitoring and early warning of the surface deformation by utilizing the InSAR technology possible.

Description

Satellite-borne SAR image data compression method
Technical Field
The invention relates to the field of time sequence InSAR data processing and analysis, in particular to a compression method of satellite-borne SAR image data.
Background
The current time sequence InSAR time sequence processing technology mainly carries out nonlinear optimization estimation through interferogram combination, has large calculation amount and needs a large amount of storage equipment. Taking a sentry satellite as an example, the design life of the satellite is 7.5 years, the repetition period is 6 days, theoretically, 500 scenes of images are adopted for the same area, if a DS processing scheme is adopted for full combination of interferograms, 124750 interference pair combinations are generated, and the time-dimension coherence matrix elements reach 500 multiplied by 500. Neither the amount of stored data nor the amount of computation is affordable by hardware devices of ordinary users.
In addition, the current SAR satellite can only acquire deformation information of a small area, and a time loss coherence phenomenon is easily caused by a long revisit time interval. In order to acquire more accurate large-scale regional deformation information by utilizing abundant SAR data and improve the accuracy of InSAR deformation estimation, in the near future, with the launching of NISAR satellites developed by the United states national aerospace administration (NASA) and the implementation of Tanden _ L satellite flight mission plans proposed by the German space navigation center (DLR), global system deformation monitoring once a week will become possible, and the satellites have shorter revisit periods, larger widths and longer operation periods, which means that InSAR will enter the big data era, and many application fields will benefit from the unprecedented data continuity, such as the early warning and forecast of geological disasters. On the other hand, the processing difficulty of the SAR data is increased along with the appearance of the satellites, and particularly, the processing amount of the InSAR data is exponentially increased along with the increase of the time dimension, so that the InSAR processing efficiency is greatly reduced. Therefore, rapidly processing these huge data is one of the key issues for implementing a real-time monitoring and early warning system. The existing time sequence InSAR processing algorithm is not suitable for operation of large data volume, the processing efficiency is low in the processing process, and the phenomenon of losing coherence is easily caused by a long time base line, so that the original potential precision of SAR data is reduced. In summary, the existing time sequence InSAR processing method has the defects of large computation amount, large storage amount, low efficiency, poor coherence and the like.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a satellite-borne SAR image data compression method for overcoming the defects of large computation amount, large storage amount, low efficiency and the like of the conventional time sequence InSAR processing method.
The purpose of the invention is mainly realized by the following technical scheme:
a compression method of spaceborne SAR image data comprises the following steps:
1) performing pile-dividing processing on an original SAR image, specifically: the registered original images are divided into a plurality of micro stacks, and an interference pattern is generated in each stack, so that the images in each small stack have high coherence, and the phenomenon of losing coherence caused by overlong time base lines can be avoided.
2) Establishing a Phase estimator, wherein in the process of time sequence InSAR processing, in order to improve the coherence and space density of Measurement Points (MPs), an effective Phase estimator is required to carry out Phase estimation on an original image, the process is called Phase-Linking, and in order to compensate the problem of possible backscatter power imbalance among all images, the invention adopts a method based on coherent matrix decomposition rather than covariance matrix decomposition to obtain original Phase information, and the specific process is as follows: performing characteristic decomposition on the coherent matrix T by using a characteristic decomposition method to obtain characteristic values and characteristic vectors of the coherent matrix, and arranging the characteristic values in a descending order, namely lambda1≥λ2≥λ3,…,≥λNT may be interpreted as the sum of N scatterer coherence matrices, where each coherence matrix represents an independent target. When the latter (N-m) eigenvectors are smaller, the phases of the first m eigenvectors can be considered to be the same as the phases of the original data vectors. Therefore, the phase values of the first m eigenvectors can be used to replace the phase values of the original data, and the noise phase and the redundant phase in the original image data can be removed by the method.
3) Establishing SAR image data compression model
Suppose there is a SAR image set Zn×lWhere n is the number of images contained in the set and l is the selected window size, then the dimension of the set in time is n-dimensional, i.e. contains n variables, trying to find m variables to replace the original n variables (m < n), and the window size l ∈ Ω remains the sameOtherwise, the original SAR image set becomes
Figure BDA0002479425500000021
The dimension of the compressed image is changed into m dimension, thereby achieving the purpose of reducing the dimension. That is, for an arbitrary orthogonal projection transform, there are:
Figure BDA0002479425500000022
for any pixel point, the phase information of the pixel point in the n scene image is assumed to be Z ═ Z (Z1,z2,z3,…,zn)HThen, the principal component analysis model is expressed as:
Figure BDA0002479425500000023
in the formula (I), the compound is shown in the specification,
Figure BDA0002479425500000024
for projecting the transformed phase information, coefficient uij(i-1, 2, …, n; j-1, 2, …, m) are projective transformation coefficients. As can be seen from the formula (2), the coefficient u can be obtainedijThe original variable Z can be converted into a new variable
Figure BDA0002479425500000027
Data compression is achieved, the efficiency of which depends on the choice of basis vectors. Under the most effective condition, the basis vectors are selected to capture the maximum variation (maximum variance) of the data space, and it can be mathematically proven that the characteristic roots of the covariance matrix of the original variables are the variances of the principal components, therefore, the first m larger characteristic roots represent the larger variance values of the first m principal components, and the characteristic vectors corresponding to the first m larger eigenvalues are the corresponding principal components
Figure BDA0002479425500000025
Coefficient u ofijI.e. a subset of the strongest eigenvectors:
＝[u1,u2,u3,…,um](3)
wherein u isj＝uijWhere (i ═ 1,2, …, n, j ═ 1,2, …, m) is a basis vector, and can be obtained by the following eigen decomposition:
Figure BDA0002479425500000026
Figure BDA0002479425500000031
is a complex coherent matrix after phase estimation, lambdaiFor characteristic values arranged in descending order, uiFor corresponding feature vectors, for limitation, u is a general termiUsing λiCorresponding unitized feature vectors, i.e. ui Hui1, the signal carrying the largest amount of information is represented by the spectral decomposition of the data space so that the largest eigenvalue corresponds to the eigenvector, and vice versa. Considering the case where the images in each mini-stack are compressed one-dimensionally in the time dimension, i.e., m is 1, the corresponding feature vector can be expressed as:
Figure BDA0002479425500000032
Subject to u1 Hu1＝1
according to the analysis, the SAR image data compression is realized by the defined spatial linear transformation:
Figure BDA0002479425500000033
projecting n-dimensional data Z to m (m < n) -dimensional data through linear transformation
Figure BDA0002479425500000034
In the linear subspace of the representation, thereby compressing the data volume.
Figure BDA0002479425500000035
Contains m compressed view complex images (SLC) ordered in sequence, so that the first oneThe row corresponds to the signal component with the largest amount of information. When m is 1, the compressed SLC is given by the following equation:
Figure BDA0002479425500000036
by the data compression method, the virtual image in each micro stack can be obtained, SAR data processing amount is greatly reduced, and time sequence InSAR processing efficiency is improved. For example, a time series of 100 SAR images can be generated if the DS method is used for time sequence analysis
Figure BDA0002479425500000037
For the interferogram, the data processing amount is very large, and along with the increase of the number of images, the data processing amount is exponentially increased, so that the targets of real-time monitoring and early warning of ground deformation are almost impossible to realize. According to the method of the invention, 100 scene images can be divided into 10 micro stacks, each stack comprises 10 scene images, and if the 10 scene images in each stack are compressed into one scene at last, the same effect can be achieved by only generating 45 pairs of interference patterns, and the data processing amount is reduced by more than 90%.
4) Generating artificial interferograms
Establishing interference pairs by using the virtual images obtained in the step 3) to generate artificial interference patterns
5) And performing time sequence InSAR processing by using the generated artificial interferogram.
After the technical scheme is adopted, compared with the prior art, the invention has the following beneficial effects.
Compared with the prior time sequence InSAR technology, the invention has the advantages that:
1) according to the method, the original SAR images are subjected to the stack division processing, high coherence exists among the images in each micro stack, and the phenomenon of losing coherence caused by overlong time base lines in the conventional time sequence InSAR technology can be overcome;
2) in the phase estimation, the coherent matrix is used for characteristic decomposition instead of the covariance matrix, and the method can compensate the possible backscattering power imbalance among all images, thereby improving the accuracy of the phase estimation;
3) the invention utilizes a data compression method to compress SAR image data in each micro stack to obtain a few virtual images carrying most information of the original SAR images in the stack, and then utilizes the virtual images to generate artificial interferograms to carry out time sequence InSAR processing, thereby greatly reducing data processing amount and memory space, improving the time sequence InSAR processing efficiency, solving the problem of InSAR big data processing and enabling real-time monitoring and early warning of ground surface deformation of an ultra-long time sequence by utilizing a time sequence InSAR technology.
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention to its proper form. It is obvious that the drawings in the following description are only some embodiments, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a flow chart of a method for compressing satellite-borne SAR image data;
FIG. 2 is a diagram of the coherence matrix for a fully assembled image and the coherence matrix in each micro stack according to an embodiment of the present invention;
FIG. 3 is an interferogram of full stack data before and after phase estimation for a minimum time (12 days) and a maximum time (426 days) in an embodiment of the present invention;
FIG. 4 is an interferogram of the micro stack before and after phase estimation for the longest time (108 days) in accordance with an embodiment of the present invention;
FIG. 5 is a diagram of an artificial interferogram and a corresponding coherence map for a maximum time (426 days) after image data compression according to an embodiment of the present invention.
It should be noted that the drawings and the description are not intended to limit the scope of the inventive concept in any way, but to illustrate it by a person skilled in the art with reference to specific embodiments.
The invention is further illustrated by the following figures and examples.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and the following embodiments are used for illustrating the present invention and are not intended to limit the scope of the present invention.
Examples
In order to verify the feasibility and effectiveness of the invention, Sentinel satellite (Sentinel-1) data is adopted for experimental analysis, and the experimental result of the invention is compared with the experimental result of full stack data. The sentinel data is used as a free resolution data source in the C wave band, is relatively suitable for large-area experimental analysis, has the wavelength of 5.63cm, the resolution of 5m multiplied by 20m and the revisit period of 12 days, and therefore, a large amount of data can be obtained in a short time by utilizing the sentinel data. And (3) selecting 50 scenes of rail-lifting data from 11 days in 2017 and 1 month to 27 days in 2018 and 9 months for experimental analysis, wherein 50 scenes of SLC images are divided into 5 micro stacks in the experimental process, and each micro stack comprises 10 scenes of images.
A satellite-borne SAR image data compression method is disclosed, the implementation flow of the technical scheme is shown as the attached figure 1, and the method comprises the following steps:
1) the original SAR images are subjected to the heap processing, 50 registered SAR images are divided into 5 micro stacks, each micro stack comprises 10 images, fig. 2(a) is a full stack data coherence matrix diagram, fig. 2(b) is a coherence matrix diagram in each micro stack, and as can be seen from fig. 2, when a time base line in the full stack coherence matrix diagram is too long, the coherence is low, and in the micro stacks, the coherence among the images is high.
2) Phase estimation of full-stack image data, in the process of time sequence InSAR processing, in order to improve coherence and spatial density of Measurement Points (MPs), Phase estimation of an original image needs to be carried out through an effective Phase estimator, the process is called Phase-Linking, and in order to compensate all imagesThe invention adopts a method based on coherent matrix decomposition instead of covariance matrix decomposition to obtain original phase information, and the specific process is as follows: performing characteristic decomposition on the full-stack data coherent matrix by using a characteristic decomposition method to obtain characteristic values and characteristic vectors of the coherent matrix, and arranging the characteristic values in a descending order, namely lambda1≥λ2≥λ3,…,≥λNT may be interpreted as the sum of N scatterer coherence matrices, where each coherence matrix represents an independent target. When the latter (N-m) eigenvectors are smaller, the phases of the first m eigenvectors can be considered to be the same as the phases of the original data vectors. Therefore, the phase values of the first m eigenvectors can be used to replace the phase values of the original data, and the results are shown in fig. 3, fig. 3(a) and 3(b) are the phase estimation front interferogram and the phase estimation back interferogram in the shortest time (12 days), respectively, and fig. 3(c) and 3(d) are the phase estimation front interferogram and the phase estimation back interferogram in the longest time (624 days), respectively, and it can be seen that the quality of the interferogram after estimation is obviously better than that of the interferogram before estimation, indicating that the PL method utilized herein has significant effects in reducing time loss correlation and enhancing SNR.
3) For each micro stack phase estimation, it can be seen from fig. 3 that the interferogram effect is better under the condition of a shorter time baseline (12 days), but the interferogram quality is obviously reduced under the condition of a longer time baseline, which indicates that the loss coherence phenomenon is more serious and is not beneficial to performing long-time sequence analysis when the time baseline in the area is longer. Therefore, 50 pieces of full-stack data are divided into 5 pieces of micro-stack data, each micro-stack contains 10 pieces of images, Phase-Linking is performed on each piece of micro-stack data, experimental parameters and procedures are the same as those of the full-stack data experiment in step 2), and a Phase estimation pre-interferogram and a Phase estimation post-interferogram in each micro-stack are obtained, as shown in fig. 4, which shows the Phase estimation pre-interferogram and the Phase estimation post-interferogram and the corresponding coherence map for the longest time (108 days) in the first micro-stack. It can be seen from the figure that the interferogram obtained after phase estimation in the micro stack has good quality, and the problem that Measurement Points (MPs) cannot be obtained due to a long time baseline and serious incoherent phenomenon when full stack data is used for processing is solved.
4) Establishing SAR image data compression model
Suppose there is a SAR image set Zn×lN is the number of images included in the set, l is the selected window size, then the dimension of the set in time is n-dimensional, that is, the set contains n variables, and m variables are sought to be found to replace the original n variables (m < n), and the window size l ∈ Ω remains unchanged, so the original SAR image set becomes the original SAR image set
Figure BDA0002479425500000051
The dimension of the compressed image is changed into m dimension, thereby achieving the purpose of reducing the dimension. That is, for an arbitrary orthogonal projection transform, there are:
Figure BDA0002479425500000052
for any pixel point, the phase information of the pixel point in the n scene image is assumed to be Z ═ Z (Z1,z2,z3,…,zn)HThen, the principal component analysis model is expressed as:
Figure BDA0002479425500000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002479425500000062
for projecting the transformed phase information, coefficient uij(i-1, 2, …, n; j-1, 2, …, m) are projective transformation coefficients. As can be seen from the formula (2), the coefficient u can be obtainedijThe original variable Z can be converted into a new variable
Figure BDA0002479425500000063
Data compression is achieved, the efficiency of which depends on the choice of basis vectors. In the most efficient case, the basis vectors are chosen to capture the maximum variation of the data space (maximum variance), which is mathematically proven to be the principal component of the eigenroots of the covariance matrix of the original variablesVariance, therefore, the first m larger feature roots represent the larger variance values of the first m principal components, and the feature vectors corresponding to the first m larger feature values are the corresponding principal components
Figure BDA0002479425500000064
Coefficient u ofijI.e. a subset of the strongest eigenvectors:
＝[u1,u2,u3,…,um](3)
wherein u isj＝uijWhere (i ═ 1,2, …, n, j ═ 1,2, …, m) is a basis vector, and can be obtained by the following eigen decomposition:
Figure BDA0002479425500000065
Figure BDA0002479425500000066
is a complex coherent matrix after phase estimation, lambdaiFor characteristic values arranged in descending order, uiFor corresponding feature vectors, for limitation, u is a general termiUsing λiCorresponding unitized feature vectors, i.e. ui Hui1, the signal carrying the largest amount of information is represented by the spectral decomposition of the data space so that the largest eigenvalue corresponds to the eigenvector, and vice versa. The present invention considers the case of compressing the images in each mini stack in one dimension in time, i.e. m is 1, then the corresponding feature vector can be expressed as:
Figure BDA0002479425500000067
Subject to u1 Hu1＝1
according to the analysis, the SAR image data compression is realized by the defined spatial linear transformation:
Figure BDA0002479425500000068
projecting n-dimensional data Z to m (m < n) -dimensional data through linear transformation
Figure BDA0002479425500000069
In the linear subspace of the representation, thereby compressing the data volume.
Figure BDA00024794255000000610
The first row corresponds to the signal component with the largest amount of information, since the compressed complex-view images (SLC) are contained in m sorted rows. When m is 1, the compressed SLC is given by the following equation:
Figure BDA00024794255000000611
by the data compression method, the virtual image in each micro stack can be obtained, and m is 1 in the embodiment of the invention to perform experimental analysis.
5) Generating artificial interferograms
Creating an interference pair by using the virtual images obtained in the step 4), generating an artificial interference map, and compressing 10 scenes of images in each micro stack into 1 scene of images in consideration of the case that m is 1. If interference processing is performed by using full stack data, 1225 interferograms are generated by 50 scene images, and data processing is performed by using the data compression method of the text, only 265 times of interference processing is required, and finally, only 10 interferograms are required to be used for time sequence analysis processing. Fig. 5 shows an artificial interferogram 5(a) and a coherence map 5(b) of the longest time baseline (624 days) obtained after the compression processing, which are compared with the phase estimation front interferogram and the phase estimation rear interferogram (fig. 3(a) and (b)) of the longest time baseline (624 days) obtained by using full stack data, it can be clearly seen that the phase estimation improves the SNR, the performances of the two schemes are highly correlated, which indicates that the data compression method herein has feasibility and effectiveness, and the data processing efficiency is improved by about 90% through the data compression processing in the aspect of efficiency, thereby greatly improving the time sequence InSAR processing efficiency.
While the foregoing is directed to the preferred embodiment of the present invention, it is not intended that the invention be limited to the embodiment and the drawings disclosed herein. Equivalents and modifications may be made without departing from the spirit of the disclosure, which is to be considered as within the scope of the invention.

Claims (3)

1. A compression method of spaceborne SAR image data is characterized by comprising the following steps:
step 1, performing pile-dividing processing on an original SAR image, dividing the registered original image into a plurality of micro stacks, and generating an interferogram in each stack;
step 2, constructing a Phase estimator, and performing Phase-Linking in each micro stack, wherein original Phase information is acquired by adopting a method based on coherent matrix decomposition:
step 3, constructing a data compression model, and compressing SAR image data in each micro stack to obtain a virtual image;
step 4, performing interference processing by using the virtual image obtained in each micro stack to generate an artificial interference pattern;
and 5, performing time sequence InSAR processing and analysis by using the generated artificial interferogram.
2. The method for compressing SAR image data according to claim 1, wherein the step 2 of obtaining the original phase information based on the coherent matrix decomposition comprises the following specific steps: performing characteristic decomposition on the coherent matrix T by using a characteristic decomposition method to obtain characteristic values and characteristic vectors of the coherent matrix, and arranging the characteristic values in a descending order, namely lambda1≥λ2≥λ3,…,≥λNT may be interpreted as the sum of N scatterer coherence matrices, where each coherence matrix represents an independent target.
3. The method for compressing spaceborne SAR image data according to claim 1, wherein the specific process of the step 3 is as follows:
subset of strongest feature vectors
＝[u1,u2,u3,…,um](3)
Wherein u isj＝uijWhere (i ═ 1,2, …, n, j ═ 1,2, …, m) is a basis vector, and can be obtained by the following eigen decomposition:
Figure FDA0002479425490000011
Figure FDA0002479425490000012
is a complex coherent matrix after phase estimation, lambdaiFor characteristic values arranged in descending order, uiFor corresponding feature vectors, ui Hui1 is ═ 1; compressing the images in each mini-stack in a one-dimensional manner in the time dimension, i.e., m is 1, the corresponding feature vector can be expressed as:
Figure FDA0002479425490000013
Subjectto u1 Hu1＝1。
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CN114994087A (en) * 2022-05-27 2022-09-02 昆明理工大学 Vegetation leaf water content remote sensing inversion method based on polarization SAR data
CN114994087B (en) * 2022-05-27 2024-05-17 昆明理工大学 Vegetation blade water content remote sensing inversion method based on polarized SAR data

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