CN117908023A - Self-adaptive four-component polarization interference synthetic aperture radar decomposition method - Google Patents
Self-adaptive four-component polarization interference synthetic aperture radar decomposition method Download PDFInfo
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Abstract
The invention provides a self-adaptive four-component polarization interference synthetic aperture radar decomposition method, and belongs to the field of radar signal processing. Step 1, two polarized SAR images are obtained for the same target area and divided into a main image and an auxiliary image, then a polarized interference coherence matrix and polarized interference similarity parameters are calculated according to the two polarized SAR images, and a self-adaptive body scattering model is constructed; step 2, calculating a polarization coherent matrix for the main image, and performing unitary transformation twice; and step 3, based on the self-adaptive bulk scattering model and the polarized coherent matrix after twice unitary transformation, sequentially calculating to obtain a spiral scattering power component, a bulk scattering power component, a surface scattering power component and a secondary scattering power component through algebraic operation. The method improves the interpretation effect on complex urban areas and forest targets, remarkably improves the problem of overestimation of the volume scattering power of the inclined building, and effectively reduces the percentage of negative power pixels after decomposition.
Description
Technical Field
The invention belongs to the field of radar signal processing, and particularly relates to a self-adaptive four-component polarization interference synthetic aperture radar decomposition method.
Background
Polarization decomposition is an important method for extracting target features from polarized synthetic aperture radar (SYNTHETIC APERTURE RADAR, SAR) data, and is widely applied to target identification and ground object classification. The model-based polarization decomposition method has clear physical meaning, but has the problems of volume scattering overestimation and negative scattering power. The negative scattering power problem can be improved by adopting an orientation angle compensation operation and a non-negative eigenvalue constraint method. Introducing orientation angles or parameters characterizing randomness into the scattering model can alleviate the problem of overestimation of the volume scattering power of a tilted building (orientation not orthogonal to the radar illumination direction). While these methods make full use of polarization information, the inclined building still introduces a large cross-polarization component, resulting in overestimation of the volume scattering power. The polarized interference SAR combines target polarization information and interference information, and is a research hotspot in the field of current SAR earth remote sensing detection. The polarization interference similarity parameter (polarimetric interferometric SIMILARITY PARAMETER, PISP) characterizes the polarization interference stability information of the target, and provides a new idea for a model-based decomposition method. However, the research on the polarization interference SAR decomposition method is less at present, and the application on the polarization interference similarity parameter is single, so that the model-based polarization interference decomposition method still remains a technical challenge.
Disclosure of Invention
In order to solve the technical problems, the invention provides a self-adaptive four-component polarization interference SAR decomposition method based on polarization interference similarity parameters.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
A self-adaptive four-component polarization interference synthetic aperture radar decomposition method comprises the following steps:
step 1, acquiring two polarized SAR images of the same target area, dividing the images into a main image and an auxiliary image, calculating a polarized interference coherence matrix and polarized interference similarity parameters according to the two polarized SAR images, and constructing a self-adaptive body scattering model;
Step 2, calculating a polarization coherent matrix for the main image, and performing unitary transformation twice;
And step 3, based on the self-adaptive bulk scattering model and the polarized coherent matrix after twice unitary transformation, sequentially calculating to obtain a spiral scattering power component, a bulk scattering power component, a surface scattering power component and a secondary scattering power component through algebraic operation.
The invention has the beneficial effects that:
(1) The self-adaptive four-component polarization interference decomposition method based on the model is provided, the interpretation effect on complex urban areas and forest targets is improved, the problem of overestimation of the volume scattering power of inclined buildings is remarkably improved, and the percentage of negative power pixels after decomposition is effectively reduced.
(2) The self-adaptive body scattering model utilizing the polarization interference similarity parameter is provided, and the size of the body scattering power component can be self-adaptively adjusted according to the coherence information of the ground object target.
Drawings
FIG. 1 is a flow chart of a model-based adaptive four-component polarization interferometry decomposition method.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
As shown in FIG. 1, a flow chart of a model-based adaptive four-component polarization interference decomposition method is provided, and first, two polarized SAR images are acquired for the same target area and divided into a main image and an auxiliary image. And then calculating a polarization interference coherence matrix according to the two polarization SAR images, further calculating polarization interference similarity parameters, and constructing a self-adaptive body scattering model. Meanwhile, a polarization coherence matrix is calculated for the main image, and twice unitary transformation is performed on the polarization coherence matrix. Finally, the spiral scattering power component, the bulk scattering power component, the surface scattering power component and the secondary scattering power component are calculated in sequence through algebraic operation.
In particular, for polarized SAR systems, a3 x 3 polarization coherence matrix is typically employed to characterize the polarization scattering characteristics of the target. Assuming reciprocity is satisfied, the polarization coherence matrix of the target can be expressed as:
（1）
wherein, Representing ensemble averaging of polarization coherence matrices,/>Elements representing row n column m in polarization coherence matrix (/ >) Superscript/>Representing conjugation. /(I)Is a target scattering vector, S is a scattering coefficient,/>Representing the horizontal polarization transmitting and horizontal polarization receiving scattering coefficient,/>Representing the vertical polarization transmitting and receiving scattering coefficient,/>Represents the vertical polarization transmission and horizontal polarization reception scattering coefficient, and the superscript T represents transposition,/>Representing the conjugate transpose of the target scatter vector,/>Representing the ensemble average.
For polarized interferometric SAR systems, a 6 x6 polarized interferometric coherence matrix is typically used to characterize the polarized interferometric scattering characteristics of the target, as follows:
（2）
wherein, And/>Target scattering vectors of two SAR images respectively,/>Representing ensemble averaging of polarization interference coherence matrices,/>And/>Representing the conjugate transpose of the target scatter vector,/>Is a non-Hermite complex coherent matrix,/>Representing the conjugate transpose of the non-hermite complex coherent matrix.
The Polarization Interference Similarity Parameter (PISP) describes the similarity of polarization information of the same ground object target in two SAR images, and can be calculated by the following formula:
（3）
wherein, Representing a coherent optimal scattering vector,/>，/>，Respectively representing 3 elements of a coherent optimal scattering vector,/>Representing a matrix trace operation,/>SuperscriptRepresenting the conjugate transpose. From equation (3), it can be seen that PISP is determined by the polarization interference optimal scattering mechanism, so this value is unique and independent of the absolute amplitudes of the two scattering targets. The more random the scattering mechanism of the target, the smaller its PISP value, e.g., vegetation areas. While larger PISP values represent more stable scattering mechanisms, such as urban building areas. Based on this, the present invention proposes an adaptive volume scattering model as follows:
（4）
wherein, Coherent matrix representing volume scattering,/>Representing the expansion coefficient of the bulk scattered power component. The model can adaptively adjust the volume scattering power in polarization decomposition according to the scattering stability of the target in the two SAR images.
The invention provides a self-adaptive four-component decomposition method based on polarization interference similarity parameters, which comprises surface scattering, secondary scattering, bulk scattering and spiral scattering mechanisms. First, a first unitary transformation is performed on the polarized coherent matrix, that is, an orientation angle compensation operation is performed on equation (1) to reduce the influence of the orientation angle, as follows:
（5）
wherein, Representing ensemble averaging of polarization coherence matrices after a first unitary transformation,/>For a rotation matrix of a first unitary transformation,/>Conjugated transpose of rotation matrix representing first unitary transform,/>Rotation angle (/ >) for first unitary transformation) Re () represents taking the real part of the complex number. And then performing a second unitary transformation on the rotated polarization coherent matrix:
（6）
wherein, Representing the polarization coherence matrix after the second unitary transformation,/>For a rotation matrix of a second unitary transformation,/>Conjugated transpose of rotation matrix representing a second unitary transform,/>Rotation angle (/ >) for a second unitary transformation) Im () represents taking the imaginary part of a complex number,/>For matrix/>Elements of row g column (/ >) J is an imaginary unit. At this time, the number of independent parameters in the polarization coherence matrix becomes 7, and polarization decomposition can be performed using the information of all the polarization coherence matrices.
Then according to the second unitary transformationA matrix, which calculates the spiral, bulk, surface and secondary scattered power components, respectively, as follows:
（7）
wherein, ，/>，/>And/>Expansion coefficients of surface scattering, secondary scattering, bulk scattering and spiral scattering power components, respectively,/>，/>，/>，/>Aggregate averaging of coherence matrices of surface, secondary, bulk and helical scattering, respectively,/>And/>Coefficients to be determined of the single scattering model and the secondary scattering model respectively, and for the convenience of calculation, the intermediate parameter/>，/>. According to equation (7), the/> can be directly obtained through algebraic operationAnd/>Corresponding spiral scattered power component/>And volume scattering power component/>：
（8）
Wherein,For matrix/>Elements of row q column of p (/ >），/>Representing an absolute value operation. Next, according to the equation (7) and the equation (8), the following equation can be obtained:
（9）
wherein S, D, C is an intermediate parameter of the calculation process.
After determining the bulk and spiral scattered power components, the power components can be determined fromJudging dominant scattering mechanism of the residual component by positive and negative, and further obtaining single scattering power component/>And a secondary scattered power component/>. Thus, let/>. When/>When surface scattering can be considered to be the dominant scattering mechanism, and the parameter/>It can be determined that/>. When/>In this case, secondary scattering can be considered as the dominant scattering mechanism, in which case the parameter/>It can be determined that/>. Accordingly, the surface scattered power component P s and the secondary scattered power component P d are obtained according to equation (9):
（10）
To this end, the surface, secondary, bulk and spiral scattering power components are all found.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.
Claims (4)
1. The self-adaptive four-component polarization interference synthetic aperture radar decomposition method is characterized by comprising the following steps of:
step 1, acquiring two polarized SAR images of the same target area, dividing the images into a main image and an auxiliary image, calculating a polarized interference coherence matrix and polarized interference similarity parameters according to the two polarized SAR images, and constructing a self-adaptive body scattering model;
Step 2, calculating a polarization coherent matrix for the main image, and performing unitary transformation twice;
And step 3, based on the self-adaptive bulk scattering model and the polarized coherent matrix after twice unitary transformation, sequentially calculating to obtain a spiral scattering power component, a bulk scattering power component, a surface scattering power component and a secondary scattering power component through algebraic operation.
2. The adaptive four-component polarized interferometric synthetic aperture radar decomposition method of claim 1, wherein said step 1 comprises:
For a polarized SAR system, a 3x 3 polarization coherence matrix is used to characterize the polarization scattering characteristics of the target, assuming reciprocity is satisfied, the polarization coherence matrix of the target is expressed as:
（1）
wherein, Representing ensemble averaging of polarization coherence matrices,/>Elements representing the nth row and column of the polarization coherence matrix,/>Superscript/>Represents conjugation; target scattering vectorS is the scattering coefficient,/>Representing the horizontal polarization transmitting and horizontal polarization receiving scattering coefficient,/>Representing the vertical polarization transmitting and receiving scattering coefficient,/>Represents the vertical polarization transmitting and horizontal polarization receiving scattering coefficient, the superscript T represents transposition, and the superscript/>Representing conjugate transpose,/>Representing the conjugate transpose of the target scatter vector,/>Representing a set average;
For a polarized interferometric SAR system, a 6×6 polarized interferometric coherence matrix is used to characterize the polarized interferometric scattering characteristics of the target, as follows:
（2）
wherein, Representing ensemble averaging of polarization interference coherence matrices,/>And/>Target scattering vectors of two polarized SAR images respectively,/>And/>Target scattering vector/>, representing two polarized SAR imagesAnd/>Conjugated transpose of/>Is a non-Hermite complex coherent matrix,/>Representing a non-hermitian complex coherent matrix/>Is a conjugate transpose of (2);
The polarization interference similarity parameter PISP describes the similarity of polarization information of the same ground object target in two polarized SAR images, and is calculated by the following formula:
（3）
wherein, Representing a coherent optimal scattering vector,/>，/>，/>Respectively representing 3 elements of a coherent optimal scattering vector,/>Representing a matrix trace operation,/>The more random the scattering mechanism of the target is, the smaller the PISP value is, the more stable the scattering mechanism of the target is, and the larger the PISP value is, so that an adaptive volume scattering model is obtained:
（4）
wherein, Coherent matrix representing volume scattering,/>Representing the expansion coefficient of the bulk scattered power component.
3. An adaptive four-component polarized interferometric synthetic aperture radar decomposition method according to claim 2, wherein said step 2 comprises:
Performing a first unitary transformation on the polarized coherent matrix, that is, performing an orientation angle compensation operation on equation (1), as follows:
（5）
wherein, Representing ensemble averaging of polarization coherence matrices after a first unitary transformation,/>For a rotation matrix of a first unitary transformation,/>Conjugated transpose of rotation matrix representing first unitary transform,/>For the rotation angle of the first unitary transformation,/>Re () represents taking the real part of complex number, performing a second unitary transformation on the rotated polarization coherence matrix:
（6）
wherein, Representing ensemble averaging of polarization coherence matrices after a second unitary transformation,/>For a rotation matrix of a second unitary transformation,/>Conjugated transpose of rotation matrix representing a second unitary transform,/>For the rotation angle of the second unitary transformation,/>Im () represents taking the imaginary part of a complex number,/>For the ensemble average/>, of the polar coherence matrix after the first unitary transformationThe element of row g, column f,/>J is an imaginary unit.
4. An adaptive four-component polarized interferometric synthetic aperture radar decomposition method according to claim 3, wherein said step 3 comprises,
Ensemble averaging of polarization coherence matrices according to second unitary transformationThe spiral, bulk, surface and secondary scattered power components were calculated separately as follows:
（7）
wherein, ，/>，/>And/>Expansion coefficients of surface scattering, secondary scattering, bulk scattering and spiral scattering power components, respectively,/>，/>，/>，/>Aggregate averaging of coherence matrices of surface, secondary, bulk and helical scattering, respectively,/>And/>Coefficients to be determined of the single scattering model and the secondary scattering model respectively, and making the intermediate parameters，/>According to the formula (7), the expansion coefficient/>, of the spiral scattering power component and the volume scattering is directly obtained through algebraic operationAnd/>Corresponding spiral scattered power component/>And volume scattering power component/>：
（8）
Wherein,For the ensemble average/>, of the polarization coherence matrix after the second unitary transformationElement of row q column/>，/>The absolute value operation is expressed, and the following equation is obtained according to the equation (7) and the equation (8):
（9）
wherein S, D, C is the intermediate parameter of the calculation process;
Order the When/>When surface scattering is the dominant scattering mechanism,/>; When (when)In the case of two scattering, the dominant scattering mechanism,/>; Obtaining the surface scattered power component/> according to equation (9)And a secondary scattered power component/>The method comprises the following steps:
（10）。
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