CN110516698B - Polarization decomposition method and device for full polarization image, electronic equipment and storage medium - Google Patents

Polarization decomposition method and device for full polarization image, electronic equipment and storage medium Download PDF

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CN110516698B
CN110516698B CN201910637291.8A CN201910637291A CN110516698B CN 110516698 B CN110516698 B CN 110516698B CN 201910637291 A CN201910637291 A CN 201910637291A CN 110516698 B CN110516698 B CN 110516698B
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王宇
禹卫东
刘秀清
王春乐
吕继宇
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Abstract

The embodiment of the invention discloses a method for polarization decomposition of a full-polarization image, which comprises the following steps: acquiring n independent hidden characteristic values of each pixel in p target pixels in a full-polarization image; performing principal component analysis on n independent hidden characteristic values of each pixel in the p target pixels to obtain n weight values respectively corresponding to the n independent hidden characteristic values; determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n; acquiring m independent hidden characteristic values corresponding to the m weight values; and for q target pixels of which the m independent hidden characteristic values are all larger than a first preset threshold value in the p target pixels, determining the volume scattering power of the q target pixels by adopting a first volume scattering model, wherein q is less than p. The embodiment of the invention also discloses a device for polarization grading of the full-polarization image, electronic equipment and a computer storage medium.

Description

Polarization decomposition method and device for full polarization image, electronic equipment and storage medium
Technical Field
The present invention relates to a target decomposition technique in the field of a polarization synthetic aperture radar, and in particular, to a method and an apparatus for polarization decomposition of a fully polarized image, an electronic device, and a computer storage medium.
Background
With the maturation of high resolution Synthetic Aperture Radar (SAR) images and polarimetric techniques, polarimetric SAR plays an important role in both the civilian and military fields. The polarized target decomposition technology is one of the important branches of the polarized SAR, and provides useful information for better understanding of a target scattering mechanism. The decomposition technique is mainly divided into two parts: model-based decomposition and eigenvalue-eigenvector-based decomposition. The model-based decomposition is directly related to a physical scattering mechanism, and the target scattering mechanism can be effectively described by decomposing a coherent matrix into combinations of various scattering components. In recent decades, many decomposition methods that improve the volume scattering model have been proposed to overcome the problems of over-estimation of the volume scattering energy and negative scattering energy, but the additional calculations make the method inefficient. Due to the diversity of natural scenes, the effectiveness of the method remains an open problem. In the existing target decomposition method, the problem of overestimation of the volume scattering energy exists.
Disclosure of Invention
In view of the above, embodiments of the present invention are directed to a method, an apparatus, an electronic device, and a computer storage medium for polarization decomposition of a fully polarized image, which at least partially solve the above problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the embodiment of the invention provides a method for polarization decomposition of a full-polarization image, which comprises the following steps:
acquiring n independent hidden characteristic values of each pixel in p target pixels in a full-polarization image;
performing principal component analysis on n independent hidden characteristic values of each pixel in the p target pixels to obtain n weight values respectively corresponding to the n independent hidden characteristic values;
determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n;
acquiring m independent hidden characteristic values corresponding to the m weight values;
and for q target pixels of which the m independent hidden characteristic values are all larger than a first preset threshold value in the p target pixels, determining the volume scattering power of the q target pixels by adopting a first volume scattering model, wherein q is less than p.
In the above scheme, the method further comprises:
and for p-q target pixels except the q target pixels in the p target pixels, determining the volume scattering power of the p-q target pixels by adopting a second volume scattering model.
In the foregoing scheme, the obtaining n independent hidden feature values of each of p target pixels in a fully-polarized image includes:
acquiring a set average value < T > of a coherence matrix of the fully polarized image,
Figure BDA0002130661900000021
wherein k is a Pauli (Pauli) group,
Figure BDA0002130661900000022
s is the scattering matrix of the fully polarized image,
Figure BDA0002130661900000023
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure BDA0002130661900000024
wherein the content of the first and second substances,
Figure BDA0002130661900000025
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure BDA0002130661900000031
Figure BDA0002130661900000032
Figure BDA0002130661900000033
Figure BDA0002130661900000034
Figure BDA0002130661900000035
Figure BDA0002130661900000036
Figure BDA0002130661900000037
wherein A represents the amplitude of the rotated coherent matrix, B represents the center of the amplitude of the rotated coherent matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a).
In the above solution, the first integral scattering model
Figure BDA0002130661900000038
The expression of (a) is:
Figure BDA0002130661900000039
where ψ represents the helix angle.
In the above aspect, the second volumetric scattering model
Figure BDA00021306619000000310
The expression of (a) is:
Figure BDA0002130661900000041
wherein, tau is a calculation process quantity, and tau is equal to<|SHH|2>/<|SVV|2>。
In the above scheme, the method further comprises:
helix angle compensation is carried out on the coherent matrix < [ T '] after the orientation angle compensation to obtain a coherent matrix < [ T' ] after the orientation angle and the helix angle compensation,
Figure BDA0002130661900000042
wherein the content of the first and second substances,
Figure BDA0002130661900000043
[R(ψ)]is a unitary transformation matrix.
In the above scheme, the method further comprises:
for the q target pixels, subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the q target pixels according to a first decision threshold C1Determining a dominant scattering mechanism;
C1＝T′11-T′22+fc/2-2Re{γ}|fw|-2m1fvwherein m is1To calculate the process quantities, m1＝(cos(4θ)-15)/60，fcIs a helical scatter component pair | SVV|2Contribution of (a) fvIs the volume scattering component pair | SVV|2Contribution of (a) fwIs the line scatter component pair | SVV| 2γ is the calculation process quantity, γ ═ SHH/SVV；
When C is present1When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present1When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
In the above scheme, the method further comprises:
for the p-q target pixels, after subtracting a line scattering component, a spiral scattering component and a volume scattering component from a coherent matrix of the p-q target pixels, determining a dominant scattering mechanism according to a second judgment threshold;
C2＝T′11-T′22+fc/2-2Re{γ}|fw|-2m2fvwherein m is2In order to calculate the amount of the process,
Figure BDA0002130661900000051
when C is present2When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present2When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
The embodiment of the invention provides a device for polarization decomposition of a full-polarization image, which comprises:
the image processing unit is used for acquiring n independent hidden characteristic values of each of p target pixels in the fully polarized image;
the main control unit is used for performing principal component analysis on n independent hidden characteristic values of each pixel in the p target pixels to obtain n weighted values respectively corresponding to the n independent hidden characteristic values; determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n; acquiring m independent hidden characteristic values corresponding to the m weight values;
and the polarization decomposition unit is used for determining the volume scattering power of q target pixels by adopting a first volume scattering model for q target pixels of which the m independent hidden characteristic values are all larger than a first preset threshold value in the p target pixels, wherein q is less than p.
In the foregoing solution, the polarization decomposition unit is further configured to: and for p-q target pixels except the q target pixels in the p target pixels, determining the volume scattering power of the p-q target pixels by adopting a second volume scattering model.
In the foregoing solution, the image processing unit is specifically configured to: acquiring a set average value < [ T ] >, of a coherent matrix of the fully polarized image,
Figure BDA0002130661900000052
wherein k is Pauli group,
Figure BDA0002130661900000053
s is the scattering matrix of the fully polarized image,
Figure BDA0002130661900000054
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure BDA0002130661900000061
wherein the content of the first and second substances,
Figure BDA0002130661900000062
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure BDA0002130661900000063
Figure BDA0002130661900000064
Figure BDA0002130661900000065
Figure BDA0002130661900000066
Figure BDA0002130661900000067
Figure BDA0002130661900000068
Figure BDA0002130661900000069
wherein A represents the amplitude of the rotated coherent matrix, B represents the center of the amplitude of the rotated coherent matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a).
In the foregoing solution, the image processing unit is further configured to: helix angle compensation is carried out on the coherent matrix < [ T '] after the orientation angle compensation to obtain a coherent matrix < [ T' ] after the orientation angle and the helix angle compensation,
Figure BDA0002130661900000071
wherein the content of the first and second substances,
Figure BDA0002130661900000072
[R(ψ)]is a unitary transformation matrix.
In the foregoing solution, the polarization decomposition unit is further configured to: for the q target pixels, subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the q target pixels according to a first decision threshold C1Determining a dominant scattering mechanism;
C1＝T′11-T′22+fc/2-2Re{γ}|fw|-2m1fvwherein m is1To calculate the process quantities, m1＝(cos(4θ)-15)/60，fcIs a helical scatter component pair | SVV|2Contribution of (a) fvIs the volume scattering component pair | SVV|2Contribution of (a) fwIs the line scatter component pair | SVV|2γ is the calculation process quantity, γ ═ SHH/SVV；
When C is present1When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present1When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
In the foregoing solution, the polarization decomposition unit is further configured to: for the p-q target pixels, after subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the p-q target pixels, determining a threshold value C according to a second determination threshold value2Determining a dominant scattering mechanism;
C2＝T′11-T′22+fc/2-2Re{γ}|fw|-2m2fvwherein m is2In order to calculate the amount of the process,
Figure BDA0002130661900000073
when C is present2When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present2When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
An embodiment of the present invention provides an electronic device, including: a transceiver, a memory, a processor, and a computer program stored on the memory and executed by the processor;
the processor is connected with the transceiver and the memory respectively, and is used for implementing any one of the above methods for polarization decomposition of the fully polarized image by executing the computer program.
The embodiment of the invention provides a computer storage medium, wherein a computer program is stored in the computer storage medium; the computer program can realize any one of the above methods for polarization decomposition of the fully polarized image after being executed.
According to the polarization decomposition method for the fully polarized image, provided by the embodiment of the invention, main component analysis is carried out on n independent hidden characteristic values of each pixel in p target pixels in the fully polarized image by acquiring the n independent hidden characteristic values of each pixel in the p target pixels, so as to obtain n weight values respectively corresponding to the n independent hidden characteristic values; determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n; acquiring m independent hidden characteristic values corresponding to the m weight values; for q target pixels of which the m independent hidden feature values are all larger than a first preset threshold value, determining the volume scattering power of the q target pixels by adopting a first volume scattering model, wherein q is less than p; the method comprises the steps of determining the volume scattering power of q target pixels by adopting a first volume scattering model for the q target pixels of which m independent hidden characteristic values are all larger than a first preset threshold value, reducing an extra volume scattering power calculation process, and solving the problem of volume scattering energy overestimation caused by the fact that the same volume scattering model is adopted for all pixels of a full-polarization image to calculate the volume scattering power in the prior art.
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The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed herein.
FIG. 1 is a flowchart illustrating a method for polarization decomposition of a fully polarized image according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for polarization decomposition of a fully polarized image according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a multi-element polarization image decomposition method according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an apparatus for polarization decomposition of a fully polarized image according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a multi-element polarization image decomposition apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
So that the manner in which the features and aspects of the embodiments of the present invention can be understood in detail, a more particular description of the embodiments of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings.
Fig. 1 is a schematic flow chart of a method for polarization decomposition of a fully polarized image according to an embodiment of the present invention, and as shown in fig. 1, the method for polarization decomposition of a fully polarized image according to an embodiment of the present invention includes the following steps:
step 101, acquiring n independent hidden feature values of each of p target pixels in a fully polarized image.
Specifically, preprocessing operations such as fine residue (Lee) filtering are carried out on the fully polarized image data to obtain the fully polarized image data from which coherent speckle noise is removed; and calculating to obtain an independent hidden characteristic value by using the fully polarized image data from which the coherent speckle noise is removed.
In some embodiments, the obtaining n independent hidden feature values for each of p target pixels in the fully-polarized image includes:
acquiring a set average value < [ T ] >, of a coherent matrix of the fully polarized image,
Figure BDA0002130661900000091
wherein k is a Pauli (Pauli) group,
Figure BDA0002130661900000092
s is the scattering matrix of the fully polarized image,
Figure BDA0002130661900000093
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure BDA0002130661900000094
wherein the content of the first and second substances,
Figure BDA0002130661900000101
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure BDA0002130661900000102
Figure BDA0002130661900000103
Figure BDA0002130661900000104
Figure BDA0002130661900000105
Figure BDA0002130661900000106
Figure BDA0002130661900000107
Figure BDA0002130661900000108
wherein A represents the amplitude of the rotated coherent matrix, B represents the center of the amplitude of the rotated coherent matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a).
Wherein the independent hidden features contain rich information related to polarization matrix rotation effects. Essentially, the characteristics of these parameters are directly related to the scattering phenomenon in rotation and can potentially reflect the characteristics of the scatterers. The method has a good characterization effect on characteristic fuzzy areas, particularly building areas with large orientation angles.
102, performing principal component analysis on n independent hidden feature values of each pixel in the p target pixels to obtain n weight values respectively corresponding to the n independent hidden feature values.
In the embodiment of the invention, the principal component analysis is a statistical method. A group of variables which are possibly correlated are converted into a group of linearly uncorrelated variables through orthogonal transformation, and the group of converted variables are called principal components. Through principal component analysis, the weight values of each group of variables in the principal component can be obtained.
Step 103, determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values except the m weight values in the n weight values, and m < n.
By selecting the m weight values with the largest weight values in the n weight values, independent hidden characteristic values with low characteristic correlation to the scatterer can be effectively eliminated.
And 104, acquiring m independent hidden characteristic values corresponding to the m weight values.
The calculation amount for judging the characteristics of the scatterer is reduced by selecting m independent hidden feature values with high characteristic correlation to the scatterer.
And 105, for q target pixels of which the m independent hidden feature values are all larger than a first preset threshold value in the p target pixels, determining the volume scattering power of the q target pixels by adopting a first volume scattering model, wherein q is less than p.
In some embodiments, the first bulk scattering model
Figure BDA0002130661900000111
The expression of (a) is:
Figure BDA0002130661900000112
where ψ represents the helix angle.
In particular, the first bulk scattering model
Figure BDA0002130661900000113
Dihedral scattering model including tilt after orientation angle compensation and phase angle compensation
Figure BDA0002130661900000114
Figure BDA0002130661900000115
In some embodiments, q target pixels of the p target pixels, of which the m independent hidden feature values are all larger than a first preset threshold value, are artificial region areas, and the method adopts
Figure BDA0002130661900000116
The body scattering power of the q target pixels is determined, and the problem of overestimation of the body scattering energy of the building area can be effectively reduced.
Fig. 2 is a schematic flow chart of a method for polarization decomposition of a fully polarized image according to an embodiment of the present invention, and as shown in fig. 2, the method for polarization decomposition of a fully polarized image according to an embodiment of the present invention includes the following steps:
step 201, acquiring n independent hidden feature values of each of p target pixels in a fully polarized image.
In some embodiments, the obtaining n independent hidden feature values for each of p target pixels in the fully-polarized image includes:
acquiring a set average value < [ T ] >, of a coherent matrix of the fully polarized image,
Figure BDA0002130661900000121
wherein k is Pauli group,
Figure BDA0002130661900000122
s is the scattering matrix of the fully polarized image,
Figure BDA0002130661900000123
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure BDA0002130661900000124
wherein the content of the first and second substances,
Figure BDA0002130661900000125
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure BDA0002130661900000131
Figure BDA0002130661900000132
Figure BDA0002130661900000133
Figure BDA0002130661900000134
Figure BDA0002130661900000135
Figure BDA0002130661900000136
Figure BDA0002130661900000137
wherein A represents the amplitude of the rotated coherent matrix, B represents the center of the amplitude of the rotated coherent matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a).
Step 202, performing principal component analysis on n independent hidden feature values of each pixel in the p target pixels to obtain n weight values respectively corresponding to the n independent hidden feature values.
Principal component analysis, which is a statistical method. A group of variables which are possibly correlated are converted into a group of linearly uncorrelated variables through orthogonal transformation, and the group of converted variables are called principal components. Through principal component analysis, the weight values of each group of variables in the principal component can be obtained.
Step 203, determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values except the m weight values in the n weight values, and m < n.
By selecting the m weight values with the largest weight values in the n weight values, independent hidden characteristic values with low characteristic correlation to the scatterer can be effectively eliminated.
And 204, acquiring m independent hidden characteristic values corresponding to the m weight values.
The calculation amount for judging the characteristics of the scatterer is reduced by selecting m independent hidden feature values with high characteristic correlation to the scatterer.
Step 205, for q target pixels, of the p target pixels, of which the m independent hidden feature values are all larger than a first preset threshold, determining the bulk scattering power of the q target pixels by using a first bulk scattering model, where q is less than p.
In some embodiments, the first bulk scattering model
Figure BDA0002130661900000141
The expression of (a) is:
Figure BDA0002130661900000142
where ψ represents the helix angle.
In particular, the first bulk scattering model
Figure BDA0002130661900000143
Dihedral scattering model including tilt after orientation angle compensation and phase angle compensation
Figure BDA0002130661900000144
Figure BDA0002130661900000145
In some embodiments, q target pixels of the p target pixels, of which the m independent hidden feature values are all larger than a first preset threshold value, are artificial region areas, and the method adopts
Figure BDA0002130661900000146
The body scattering power of the q target pixels is determined, and the problem of overestimation of the body scattering energy of the building area can be effectively reduced.
And step 206, for p-q target pixels except the q target pixels in the p target pixels, determining the volume scattering power of the p-q target pixels by adopting a second volume scattering model.
In some embodiments, the second volumetric scattering model
Figure BDA0002130661900000147
The expression of (a) is:
Figure BDA0002130661900000148
wherein, tau is a meterCalculating the process quantity, τ ═<|SHH|2>/<|SVV|2>And ψ denotes a helix angle.
In particular, the second volumetric scattering model
Figure BDA0002130661900000151
Including general volume scattering model after orientation angle compensation and phase angle compensation
Figure BDA0002130661900000152
Figure BDA0002130661900000153
In some embodiments, p-q target pixels of the p target pixels except the q target pixels are natural ground object regions, and the method adopts
Figure BDA0002130661900000154
And determining the volume scattering power of the p-q target pixels, so that the volume scattering energy of the natural ground object region can be correctly characterized.
In some embodiments, the helix angle compensation is performed on the orientation angle compensated coherence matrix < [ T '] > to obtain an orientation angle and helix angle compensated coherence matrix < [ T' ],
Figure BDA0002130661900000155
wherein the content of the first and second substances,
Figure BDA0002130661900000156
[R(ψ)]is a unitary transformation matrix.
Step 207, for the q target pixels, subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the q target pixels, and then determining a threshold value C according to a first determination threshold value1Determining a dominant scattering mechanism;
C1＝T′11-T′22+fc/2-2Re{γ}|fw|-2m1fvwherein m is1To calculate the process quantities, m1＝(cos(4θ)-15)/60，fcIs a helical scatter component pair | SVV|2Contribution of (a) fvIs the volume scattering component pair | SVV|2Contribution of (a) fwIs the line scatter component pair | SVV|2γ is the calculation process quantity, γ ═ SHH/SVV；
Specifically, when C1When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present1When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
In some embodiments, the q target pixels are building regions according to the first decision threshold C1Determining the dominant scattering mechanism may reduce overestimation of the volume scattering for the building region.
Step 208, for the p-q target pixels, after subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the p-q target pixels, determining a threshold value C according to a second determination threshold value2Determining a dominant scattering mechanism;
C2＝T′11-T′22+fc/2-2Re{γ}|fw|-2m2fvwherein m is2In order to calculate the amount of the process,
Figure BDA0002130661900000161
specifically, when C2When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present2When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
Fig. 3 is a schematic flow chart of a multi-element polarization image decomposition method according to an embodiment of the present invention, and as shown in fig. 3, an embodiment of the present invention is based on the above method for polarization decomposition of a fully polarized image, and an exemplary provided multi-element polarization image decomposition method includes the following steps:
step 301: and calculating an independent hidden characteristic value by utilizing the full-polarization image data acquired by the polarized synthetic aperture radar.
Specifically, preprocessing operations such as fine Lee filtering are performed on the polarized image data to obtain image data from which speckle noise is removed.
The scattering matrix of the fully polarized image data is
Figure BDA0002130661900000162
Pauli group is:
Figure BDA0002130661900000163
according to the relation between Pauli base and coherent matrix, the aggregate average value < [ T ] > of coherent matrix of the polarization image is obtained as follows:
Figure BDA0002130661900000164
the coherent matrix < [ T' ] after orientation angle compensation is:
Figure BDA0002130661900000171
wherein [ R (θ) ] is a unitary rotation matrix
Figure BDA0002130661900000172
Theta represents an orientation angle of the liquid crystal,
Figure BDA0002130661900000173
the independent hidden features are existing feature parameters, and specifically, the expression is as follows:
Figure BDA0002130661900000174
Figure BDA0002130661900000175
Figure BDA0002130661900000176
Figure BDA0002130661900000177
Figure BDA0002130661900000178
Figure BDA0002130661900000179
Figure BDA00021306619000001710
wherein A represents the amplitude of the rotated coherent matrix, B represents the center of the amplitude of the rotated coherent matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a).
The independent hidden features contain rich information about the polarization matrix rotation effect. The characteristics of these parameters are essentially directly related to the scattering phenomena in the rotational domain and may potentially reflect the characteristics of the scatterers. The method has a good characterization effect on the characteristic fuzzy area, particularly the building area with a large included angle with the radar sight line direction.
Step 302: and performing principal component analysis by using the hidden features, selecting three hidden feature values which have the best characteristics on the artificial land area, performing artificial land object area extraction by using a histogram threshold method, and obtaining a binary extraction result.
Specifically, principal component analysis is carried out on all the independent hidden features, the weight occupied by each independent hidden feature is obtained through calculation, and three optimal hidden feature values are selected as the basis for extracting the buildings in the urban area according to the obtained weights.
The extraction of the buildings in the urban area is realized through a histogram threshold method, specifically, partial pixel points of known ground objects are selected as training samples, hidden feature values of the training sample points are counted in a histogram mode, and the extraction of the artificial ground object area is completed through selecting a proper threshold value.
The binary extraction result is as follows: when the value is larger than the selected threshold value, the logic value of the pixel is set to be 1, and the pixel belongs to the artificial ground object area; and when the value is smaller than the selected threshold value, the logic value of the pixel is set to be 0, and the pixel point belongs to the natural ground object area. And obtaining a binary extraction result after the logic values of all the pixels are determined.
Step 303: and selecting a corresponding improved volume scattering model by using the binary extraction result.
Specifically, the binary extraction result may be used to classify the type of the region by two: when the extraction result value is 1, the pixel point belongs to an artificial ground object region, and the first integral scattering model is selected as the integral scattering model; and when the extraction result value is 0, the pixel point belongs to a natural ground object region, and the second volume scattering model is selected as the volume scattering model.
In the present invention, the coherence matrix for multi-element decomposition requires an orientation angle compensation operation and a helix angle compensation operation before decomposition.
The coherent matrix after orientation angle compensation and helix angle compensation is:
Figure BDA0002130661900000181
wherein the content of the first and second substances,
Figure BDA0002130661900000182
in the above equation, [ phi ] denotes a spiral angle, and [ R (phi) ] is a unitary transformation matrix.
After the phase angle compensation and the spiral angle compensation are completed, the element T23Real part and T of13The imaginary part of (A) will become 0, the unknown elements are changed from nine to seven, the number of the unknown quantity needing to be solved is reduced, and simultaneously, T is better minimized33And (4) taking the value of the element.
The improved bulk scattering model for characterizing different surface feature types comprises a first bulk scattering model and a second bulk scattering model which are subjected to orientation angle compensation and phase angle compensation.
The initial expression of the volume scattering model is as follows:
Figure BDA0002130661900000191
Figure BDA0002130661900000192
wherein τ ═<|SHH|2>/<|SVV|2>And theta represents the aforementioned orientation angle value,
Figure BDA0002130661900000193
and
Figure BDA0002130661900000194
representing a first and a second volumetric scattering model, respectively.
When the pixel belongs to the artificial ground object region, the first body scattering model after orientation angle compensation and phase angle compensation is selected as the body scattering model, and the specific expression of the model is as follows:
Figure BDA0002130661900000195
when the pixel point belongs to a natural ground object region, the second volume scattering model after orientation angle compensation and phase angle compensation is selected as the volume scattering model, and the specific expression of the model is as follows:
Figure BDA0002130661900000196
where θ and ψ denote the orientation angle and the helix angle, respectively.
Step 304: and obtaining a multi-element decomposition result by using the selected scattering model and the image data.
Specifically, the bulk scattering power is calculated using the selected scattering model, and the power values of the remaining scattering components are calculated using a multi-element decomposition method.
Compared with the existing multi-element decomposition method, the multi-element decomposition method is different in that the method is used for judging the value of the threshold value of the dihedral angle/surface scattering occupying body. Specifically, after subtracting the line scattering component, the spiral scattering component and the volume scattering component, the coherence matrix is used to determine that the dihedral angle/surface scattering accounts for the main body, and the threshold value is:
C＝T′11-T′22+fc/2-2Re{γ}|fw|-2mfv
wherein f isc，fvAnd fwRepresenting the helical, volume and line scatter component pairs | SVV|2Contribution of (1), γ ═ SHH/SVV. If the first volume scattering model is selected, m is (cos (4 θ) -15)/60, whereas if the second volume scattering model is selected, the second volume scattering model is selected
Figure BDA0002130661900000201
Fig. 4 is a schematic structural diagram of a device for polarization decomposition of a fully polarized image according to an embodiment of the present invention, and as shown in fig. 4, the device for polarization decomposition of a fully polarized image according to an embodiment of the present invention includes: image processing unit 401, main control unit 402 and polarization decomposition unit 403, wherein:
an image processing unit 401, configured to obtain n independent hidden feature values of each of p target pixels in the fully-polarized image.
In some embodiments, the image processing unit 401 is specifically configured to:
acquiring a set average value < [ T ] >, of a coherent matrix of the fully polarized image,
Figure BDA0002130661900000202
wherein k is Pauli group,
Figure BDA0002130661900000203
s is the scattering matrix of the fully polarized image,
Figure BDA0002130661900000204
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure BDA0002130661900000211
wherein the content of the first and second substances,
Figure BDA0002130661900000212
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure BDA0002130661900000213
Figure BDA0002130661900000214
Figure BDA0002130661900000215
Figure BDA0002130661900000216
Figure BDA0002130661900000217
Figure BDA0002130661900000218
Figure BDA0002130661900000219
wherein A represents the amplitude of the rotated coherent matrix, B represents the center of the amplitude of the rotated coherent matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a).
A main control unit 402, configured to perform principal component analysis on n independent hidden feature values of each pixel of the p target pixels, to obtain n weight values respectively corresponding to the n independent hidden feature values; determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n; and acquiring m independent hidden characteristic values corresponding to the m weight values.
Principal component analysis, which is a statistical method. A group of variables which are possibly correlated are converted into a group of linearly uncorrelated variables through orthogonal transformation, and the group of converted variables are called principal components. Through principal component analysis, the weight values of each group of variables in the principal component can be obtained.
By selecting the m weight values with the largest weight values in the n weight values, independent hidden characteristic values with low characteristic correlation to the scatterer can be effectively eliminated.
The calculation amount for judging the characteristics of the scatterer is reduced by selecting m independent hidden feature values with high characteristic correlation to the scatterer.
A polarization decomposition unit 403, configured to determine, for q target pixels, of the p target pixels, where the m independent hidden feature values are all greater than a first preset threshold, a bulk scattering power of the q target pixels by using a first bulk scattering model, where q is less than p.
In some embodiments, the first bulk scattering model
Figure BDA0002130661900000221
The expression of (a) is:
Figure BDA0002130661900000222
where ψ represents the helix angle.
In particular, the first bulk scattering model
Figure BDA0002130661900000223
Dihedral scattering model including tilt after orientation angle compensation and phase angle compensation
Figure BDA0002130661900000224
Figure BDA0002130661900000225
In some embodiments, the p target pixels are the same as the target pixelsQ target pixels with m independent hidden characteristic values larger than a first preset threshold are used as building areas, and
Figure BDA0002130661900000226
the body scattering power of the q target pixels is determined, and the problem of overestimation of the body scattering energy of the building area can be effectively reduced.
In some embodiments, the polarization decomposition unit 403 is further configured to determine, for p-q target pixels of the p target pixels except for the q target pixels, a bulk scattering power of the p-q target pixels by using a second bulk scattering model.
In some embodiments, the second volumetric scattering model
Figure BDA0002130661900000231
The expression of (a) is:
Figure BDA0002130661900000232
wherein, tau is a calculation process quantity, and tau is equal to<|SHH|2>/<|SVV|2>And ψ denotes a helix angle.
In particular, the second volumetric scattering model
Figure BDA0002130661900000233
Including general volume scattering model after orientation angle compensation and phase angle compensation
Figure BDA0002130661900000234
Figure BDA0002130661900000235
In some embodiments, p-q target pixels of the p target pixels except the q target pixels are natural ground object regions, and the method adopts
Figure BDA0002130661900000236
And determining the volume scattering power of the p-q target pixels, so that the volume scattering energy of the natural ground object region can be correctly characterized.
In some embodiments, the image processing unit 401 is further configured to: helix angle compensation is carried out on the coherent matrix < [ T '] after the orientation angle compensation to obtain a coherent matrix < [ T' ] after the orientation angle and the helix angle compensation,
Figure BDA0002130661900000237
wherein the content of the first and second substances,
Figure BDA0002130661900000238
[R(ψ)]is a unitary transformation matrix.
In some embodiments, the polarization decomposition unit 403 is further configured to: for the q target pixels, subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the q target pixels according to a first decision threshold C1Determining a dominant scattering mechanism;
C1＝T′11-T′22+fc/2-2Re{γ}|fw|-2m1fvwherein m is1To calculate the process quantities, m1＝(cos(4θ)-15)/60，fcIs a helical scatter component pair | SVV|2Contribution of (a) fvIs the volume scattering component pair | SVV|2Contribution of (a) fwIs the line scatter component pair | SVV|2γ is the calculation process quantity, γ ═ SHH/SVV；
Specifically, when C1When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present1When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
In some embodiments, the q target pixels are building regions according to the first decision threshold C1Determining dominant scattering mechanisms may reduce the area of a buildingVolume scatter energy over-estimation.
In some embodiments, the polarization decomposition unit 403 is further configured to: for the p-q target pixels, after subtracting a line scattering component, a spiral scattering component and a volume scattering component from a coherent matrix of the p-q target pixels, determining a dominant scattering mechanism according to a second judgment threshold;
C2＝T′11-T′22+fc/2-2Re{γ}|fw|-2m2fvwherein m is2In order to calculate the amount of the process,
Figure BDA0002130661900000241
specifically, when C2When the scattering component is more than 0, the surface scattering component is the dominant scattering mechanism; when C is present2When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
In some embodiments, the p-q target pixels are natural ground object regions, and the second determination threshold C is used for determining the second determination threshold C2And the dominant scattering mechanism is determined, so that the scattering mechanism of the natural feature region can be correctly characterized.
Fig. 5 is a schematic structural diagram of a multi-element polarization image decomposition apparatus according to an embodiment of the present invention, and as shown in fig. 5, the multi-element polarization image decomposition apparatus according to an embodiment of the present invention includes: a parameter calculation module 501, an optimization extraction module 502, a selection module 503, and a decomposition module 504, wherein:
the parameter calculation module 501 is configured to perform preprocessing operations such as refined Lee filtering on the polarization image data, calculate independent hidden feature values, and send the calculated values of all the independent hidden features to the optimization extraction module.
And an optimization extraction module 502, configured to perform data dimension reduction by using the independent hidden features sent by the parameter calculation module 501, optimize features used for extracting the artificial feature region, further obtain an extraction result of the artificial feature region, and send the obtained binary extraction result of the artificial feature region to the selection module.
A selecting module 503, configured to select a corresponding improved bulk scattering model according to the binary extraction result of the artificial feature sent from the optimization extracting module 502, and send the selected bulk scattering model to the decomposing module 504.
And a decomposition module 504, configured to obtain a multi-element decomposition result by using the selected volume scattering model and the image data sent from the selection module 503.
The parameter calculation module 501 is specifically configured to perform preprocessing operations such as refined Lee filtering on the polarization image data to obtain image data with speckle noise removed; and calculating to obtain an independent hidden characteristic value by using the image data with the speckle noise removed.
The independent hidden features are feature parameters which are already proposed, and a specific expression is as follows:
Figure BDA0002130661900000251
Figure BDA0002130661900000252
Figure BDA0002130661900000253
Figure BDA0002130661900000254
Figure BDA0002130661900000255
Figure BDA0002130661900000256
Figure BDA0002130661900000257
wherein the independent hidden features contain rich information related to polarization matrix rotation effects. The characteristics of these parameters are essentially directly related to the scattering phenomena in the rotational domain and may potentially reflect the characteristics of the scatterers. The method has a good characterization effect on characteristic fuzzy areas, particularly building areas with large orientation angles.
The optimization extraction module 502 is specifically configured to perform principal component analysis on all the independent hidden features, calculate a weight occupied by each independent hidden feature, and select three optimal hidden feature values as bases for extracting buildings in the urban area according to the obtained weights.
The extraction of the buildings in the urban area is realized through a histogram threshold method, specifically, partial pixel points of known ground objects are selected as training samples, hidden feature values of the training sample points are counted through the histogram, and a proper threshold value is selected to finish the extraction of the artificial ground object area.
The binary extraction result means that if the value of the hidden feature of the pixel is larger than the selected threshold, the logical value of the pixel is set to be 1, and the pixel belongs to the artificial ground object area; and when the value is smaller than the selected threshold value, the logic value of the pixel is set to be 0, and the pixel point belongs to the natural ground object area. And after the logic values of all the pixel points are determined, obtaining a binary extraction result.
The selecting module 503 is specifically configured to classify the types of the regions into two categories: when the extraction result value is 1, the pixel point belongs to an artificial ground object region, and the first integral scattering model subjected to angle compensation is selected as the integral scattering model; and when the extraction result value is 0, the pixel point belongs to a natural ground object region, and the second volume scattering model after angle compensation is selected as the volume scattering model.
When the selection module 503 performs the action, the coherence matrix for multi-element decomposition needs to be subjected to an orientation angle compensation operation and a helix angle compensation operation before decomposition.
After the angle compensation operation is completed, the element T23Real part and T of13The imaginary part of (1) can be changed into 0, the unknown elements are changed into seven from the original nine, the number of the unknown quantities needing to be solved is reduced, and meanwhile, the imaginary part of (1) is changed into sevenPreferably minimize T33And (4) taking the value of the element.
The improved bulk scattering model for characterizing different surface feature types comprises a first bulk scattering model and a second bulk scattering model which are subjected to orientation angle compensation and phase angle compensation.
The initial expression of the volume scattering model is as follows:
Figure BDA0002130661900000261
Figure BDA0002130661900000262
the orientation angle compensation process is written as: [ T' ] > [ R (theta) ] < [ T ] > [ R (theta) ]
The helix angle compensation process is written as: [ T '] > [ R (psi) ] < [ T' ]) [ R (psi) ]
Wherein the content of the first and second substances,
Figure BDA0002130661900000271
Figure BDA0002130661900000272
when the pixel point belongs to a natural ground object region, the second volume scattering model after orientation angle compensation and phase angle compensation is selected as the volume scattering model, and the specific expression of the model is as follows:
Figure BDA0002130661900000273
when the pixel belongs to the artificial ground object region, the first body scattering model after orientation angle compensation and phase angle compensation is selected as the body scattering model, and the specific expression of the model is as follows:
Figure BDA0002130661900000274
wherein τ ═<|SHH|2>/<|SVV|2>And θ and ψ denote rotation angles, and specific expressions have been given above.
The decomposition module 504 is specifically configured to utilize the selected scattering model and calculate power values of the remaining scattering components by using a multi-element decomposition method.
Compared with the existing multi-element decomposition method, the multi-element decomposition method is different in that the method is used for judging the value of the threshold value of the dihedral angle/surface scattering occupying body. Specifically, after subtracting the line scattering component, the spiral scattering component and the volume scattering component, the coherence matrix is used to determine that the dihedral angle/surface scattering accounts for the main body, and the threshold value is:
C＝T′11-T′22+fc/2-2Re{γ}|fw|-2mfv
wherein, if the pixel point to be solved belongs to the natural ground object region, the volume scattering model selects an improved second volume scattering model,
Figure BDA0002130661900000275
if the pixel point to be solved belongs to the artificial ground object region, the improved first integral scattering model is selected as the integral scattering model, and m is (cos (4 theta) -15)/60. The decision threshold used in the method may be adapted to different ground feature types.
In order to implement the method for polarization decomposition of a fully polarized image according to the embodiment of the present invention, an embodiment of the present invention provides an electronic device with a schematic structural diagram shown in fig. 6, and as shown in fig. 6, an electronic device 610 according to the embodiment of the present invention includes: a processor 61 and a memory 62 for storing computer programs capable of running on the processor, wherein,
the processor 61 is configured to execute the steps of any one of the methods for polarization decomposition of a fully polarized image according to the foregoing embodiments of the present invention when the computer program is executed.
Of course, in practical applications, as shown in fig. 6, the electronic device may further include at least one communication interface 63. The various components in the electronic device are coupled together by a bus system 64. It will be appreciated that the bus system 64 is used to enable communications among the components. The bus system 64 includes a power bus, a control bus, and a status signal bus in addition to the data bus. For clarity of illustration, however, the various buses are labeled as bus system 64 in fig. 6.
Among other things, a communication interface 63 for interacting with other devices.
Specifically, the processor 61 may send an operation result query request to an application server corresponding to the callee application through the communication interface 63, and obtain an operation result of the callee application sent by the application server.
Those skilled in the art will appreciate that the memory 62 may be either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 62 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
In an embodiment of the present invention, a computer-readable storage medium is further provided, which is used for storing the calculation program provided in the foregoing embodiment, so as to complete the steps of the method for polarization decomposition of the fully polarized image. The computer readable storage medium can be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories, such as mobile phones, computers, smart appliances, servers, etc.
The methods disclosed in the several method embodiments provided in the present application may be combined arbitrarily without conflict to obtain new method embodiments.
The features disclosed in the several method or device embodiments provided by the present invention may be combined arbitrarily, without conflict, to arrive at new method or device embodiments.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (12)

1. A method of polarization decomposition of a fully polarized image, the method comprising:
acquiring n independent hidden characteristic values of each pixel in p target pixels in a full-polarization image; the acquiring n independent hidden feature values of each of p target pixels in a fully-polarized image includes:
acquiring a set average value < [ T ] >, of a coherent matrix of the fully polarized image,
Figure FDA0003402049470000011
wherein k is a Pauli group,
Figure FDA0003402049470000012
s is the scattering matrix of the fully polarized image,
Figure FDA0003402049470000013
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure FDA0003402049470000014
wherein the content of the first and second substances,
Figure FDA0003402049470000015
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure FDA0003402049470000016
Figure FDA0003402049470000017
Figure FDA0003402049470000021
Figure FDA0003402049470000022
Figure FDA0003402049470000023
Figure FDA0003402049470000024
Figure FDA0003402049470000025
wherein A represents the amplitude of the coherence matrix, B represents the center of the amplitude of the coherence matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a);
performing principal component analysis on n independent hidden characteristic values of each pixel in the p target pixels to obtain n weight values respectively corresponding to the n independent hidden characteristic values;
determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n;
acquiring m independent hidden characteristic values corresponding to the m weight values;
for said p target pixelsDetermining the volume scattering power of q target pixels by adopting a first integral scattering model, wherein the m independent hidden characteristic values are all larger than q target pixels of a first preset threshold value<p, the first bulk scattering model
Figure FDA0003402049470000026
The expression of (a) is:
Figure FDA0003402049470000027
wherein ψ represents a helix angle;
and for p-q target pixels except the q target pixels in the p target pixels, determining the volume scattering power of the p-q target pixels by adopting a second volume scattering model.
2. The method of claim 1, wherein the second volumetric scattering model
Figure FDA0003402049470000031
The expression of (a) is:
Figure FDA0003402049470000032
wherein, tau is a calculation process quantity, and tau is equal to<|SHH|2>/<|SVV|2>。
3. The method of claim 2, further comprising:
helix angle compensation is carried out on the coherent matrix < [ T '] after the orientation angle compensation to obtain a coherent matrix < [ T' ] after the orientation angle and the helix angle compensation,
Figure FDA0003402049470000033
wherein the content of the first and second substances,
Figure FDA0003402049470000034
[R(ψ)]is a unitary transformation matrix.
4. The method of claim 3, further comprising:
for the q target pixels, subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the q target pixels according to a first decision threshold C1Determining a dominant scattering mechanism;
C1＝T′11-T′22+fc/2-2Re{γ}|fw|-2m1fvwherein m is1To calculate the process quantities, m1＝(cos(4θ)-15)/60，fcIs a helical scatter component pair | SVV|2Contribution of (a) fvIs the volume scattering component pair | SVV|2Contribution of (a) fwIs the line scatter component pair | SVV|2γ is the calculation process quantity, γ ═ SHH/SVV；
When C is present1>At 0, the surface scattering component is the dominant scattering mechanism; when C is present1When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
5. The method of claim 4, further comprising:
for the p-q target pixels, after subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the p-q target pixels, determining a threshold value C according to a second determination threshold value2Determining a dominant scattering mechanism;
C2＝T′11-T′22+fc/2-2Re{γ}|fw|-2m2fvwherein m is2In order to calculate the amount of the process,
Figure FDA0003402049470000041
when C is present2>At 0, the surface scattering component is the dominant scattering mechanism; when C is present2When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
6. An apparatus for polarization decomposition of a fully polarized image, the apparatus comprising:
the image processing unit is used for acquiring n independent hidden characteristic values of each of p target pixels in the fully polarized image; the acquiring n independent hidden feature values of each of p target pixels in a fully-polarized image includes:
acquiring a set average value < [ T ] >, of a coherent matrix of the fully polarized image,
Figure FDA0003402049470000042
wherein k is a Pauli group,
Figure FDA0003402049470000043
s is the scattering matrix of the fully polarized image,
Figure FDA0003402049470000044
wherein S isHHBackscatter for vertical transmission, vertical reception, SHVBackscatter for vertical transmission, horizontal reception, SVHBackscatter for horizontal transmission, vertical reception, SVVBackscattering for horizontal transmission, horizontal reception;
carrying out orientation angle compensation on the coherent matrix to obtain a coherent matrix subjected to orientation angle compensation,
Figure FDA0003402049470000051
wherein the content of the first and second substances,
Figure FDA0003402049470000052
theta represents an orientation angle, [ R (theta) ]]Is a unitary rotation matrix;
the n independent hidden feature values include:
Figure FDA0003402049470000053
Figure FDA0003402049470000054
Figure FDA0003402049470000055
Figure FDA0003402049470000056
Figure FDA0003402049470000057
Figure FDA0003402049470000058
Figure FDA0003402049470000059
wherein A represents the amplitude of the coherence matrix, B represents the center of the amplitude of the coherence matrix, and θ0Represents the initial phase value, ω represents the angular frequency; re [. C]Representing the real part, Im [. cndot]Representing the imaginary component, Angle {. is } used to obtain the phase values of the elements in the complex axis; a _ T'ijRepresents T'ijAmplitude of (1), B _ T'ijRepresents T'ijCenter of amplitude of, theta0_T′ijRepresents T'ijThe initial phase value of (a);
the main control unit is used for performing principal component analysis on n independent hidden characteristic values of each pixel in the p target pixels to obtain n weighted values respectively corresponding to the n independent hidden characteristic values; determining m weight values of the n weight values, wherein the m weight values are all larger than n-m weight values of the n weight values except the m weight values, and m < n; acquiring m independent hidden characteristic values corresponding to the m weight values;
a polarization decomposition unit, configured to determine, for q target pixels, of which m independent hidden feature values are greater than a first preset threshold, the bulk scattering power of the q target pixels by using a first bulk scattering model, where q is the number of the m independent hidden feature values<p, the first bulk scattering model
Figure FDA0003402049470000061
The expression of (a) is:
Figure FDA0003402049470000062
where ψ represents the helix angle.
7. The apparatus of claim 6, wherein the polarization splitting unit is further configured to: and for p-q target pixels except the q target pixels in the p target pixels, determining the volume scattering power of the p-q target pixels by adopting a second volume scattering model.
8. The apparatus of claim 6, wherein the image processing unit is further configured to: helix angle compensation is carried out on the coherent matrix < [ T '] after the orientation angle compensation to obtain a coherent matrix < [ T' ] after the orientation angle and the helix angle compensation,
Figure FDA0003402049470000063
wherein the content of the first and second substances,
Figure FDA0003402049470000064
[R(ψ)]is a unitary transformation matrix.
9. The apparatus of claim 6, wherein the polarization splitting unit is further configured to: for the q target pixels, subtracting the line scattering component, the spiral scattering component and the volume scattering component from the coherent matrix of the q target pixels according to a first decision threshold C1Determining a dominant scattering mechanism;
C1＝T′11-T′22+fc/2-2Re{γ}|fw|-2m1fvwherein m is1To calculate the process quantities, m1＝(cos(4θ)-15)/60，fcIs a helical scatter component pair | SVV|2Contribution of (a) fvIs the volume scattering component pair | SVV|2Contribution of (a) fwIs the line scatter component pair | SVV|2γ is the calculation process quantity, γ ═ SHH/SVV；
When C is present1>At 0, the surface scattering component is the dominant scattering mechanism; when C is present1When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
10. The apparatus of claim 7, wherein the polarization splitting unit is further configured to: for the p-q target pixels, after subtracting a line scattering component, a spiral scattering component and a volume scattering component from a coherent matrix of the p-q target pixels, determining a dominant scattering mechanism according to a second judgment threshold;
C2＝T′11-T′22+fc/2-2Re{γ}|fw|-2m2fvwherein m is2In order to calculate the amount of the process,
Figure FDA0003402049470000071
when C is present2>At 0, the surface scattering component is the dominant scattering mechanism; when C is present2When the scattering rate is less than or equal to 0, the dihedral angle scattering is the dominant scattering mechanism.
11. An electronic device, comprising: a transceiver, a memory, a processor, and a computer program stored on the memory and executed by the processor;
the processor, connected to the transceiver and the memory respectively, is configured to implement the method for polarization decomposition of fully polarized images according to any one of claims 1 to 5 by executing the computer program.
12. A computer storage medium storing a computer program; the computer program, when executed, is capable of implementing a method of full polarization image polarization decomposition as claimed in any one of claims 1 to 5.
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