CN112597434A - Rapid implementation method and system for polarized KHT decomposition - Google Patents

Rapid implementation method and system for polarized KHT decomposition Download PDF

Info

Publication number
CN112597434A
CN112597434A CN202011223258.XA CN202011223258A CN112597434A CN 112597434 A CN112597434 A CN 112597434A CN 202011223258 A CN202011223258 A CN 202011223258A CN 112597434 A CN112597434 A CN 112597434A
Authority
CN
China
Prior art keywords
decomposition
huynen
parameters
target
polarization
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011223258.XA
Other languages
Chinese (zh)
Other versions
CN112597434B (en
Inventor
李东
张云华
梁莉婷
杨杰芳
王勋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
National Space Science Center of CAS
Original Assignee
National Space Science Center of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by National Space Science Center of CAS filed Critical National Space Science Center of CAS
Priority to CN202011223258.XA priority Critical patent/CN112597434B/en
Publication of CN112597434A publication Critical patent/CN112597434A/en
Application granted granted Critical
Publication of CN112597434B publication Critical patent/CN112597434B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/024—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using polarisation effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Abstract

The invention discloses a method and a system for quickly realizing polarization KHT decomposition, wherein the method comprises the following steps: reading in a polarization scattering matrix S to be decomposed]Calculating Huynen parameters according to data; calculating Kennaugh-Huynen decomposition parameters based on the Huynen parameters, the Kennaugh-Huynen decomposition parameters comprising: the method comprises the following steps of (1) obtaining a target maximum scattering amplitude m, an orientation angle psi, a helical angle tau, a polarization angle gamma and a jump angle upsilon; calculating Touzi decomposition parameters based on the Huynen parameters, wherein the Touzi decomposition parameters comprise: symmetric scattering amplitude alphasAnd symmetric scattering phase
Figure DDA0002762791460000011
The method of the invention allows the rapid implementation of the decomposition of polarized KHT.

Description

Rapid implementation method and system for polarized KHT decomposition
Technical Field
The invention relates to the field of polarized radar image information processing, in particular to the field of polarized coherent radar target decomposition, and particularly relates to a method and a system for quickly realizing polarized KHT decomposition.
Background
The polarized radar detects diverse polarized scattering information of a target through diverse polarization states formed by transmitting and receiving antennas and stores the information in a 2 x 2 target scattering matrix S. Polarization coherent decomposition is aimed at extracting geometric physical scattering information of an object from a matrix [ S ] for enhancing detection and identification interpretation. In 1951, professor Kennaugh, ohio state university, usa, found that extraction of a characteristic polarization state of interest could be achieved by diagonalization of the scattering matrix [ S ]. In 1970, Dr Huynen, Darworv, the Netherlands, was further perfected theoretically by Huynen, Inc., which is known in the academia as Kennaugh-Huynen decomposition. Based on the decomposition, Huynen develops a famous radar target phenomenological theory and lays a foundation for radar polarization detection.
The Kennaugh-Huynen decomposition is a special form of the Autonne-Takagi decomposition, which is described as follows:
Figure BDA0002762791440000011
unitary matrix U2(υ，τ，ψ)]Further composed of three matrices belonging to a second-order special unitary group SU (2):
[U2(υ，τ，ψ)]＝[U2(υ)][U2(τ)][U2(ψ)]
here, the
Figure BDA0002762791440000012
Wherein [ U ]2(ψ)]For SU (2) polarization rotation matrix, #∈[-90°，90°]Is the target direction angle; [ U ]2(τ)]Is a SU (2) polarization ellipse matrix, and tau epsilon [ -45 DEG, 45 DEG]A target elliptical fillet is taken; [ U ]2(υ)]Is the SU (2) polarization phase matrix, upsilon ∈ [ -45 DEG, 45 DEG]Is the target jump angle.
[SD(m，γ)]For the real diagonal matrix:
Figure BDA0002762791440000013
wherein m is the maximum scattering amplitude of the target; gamma epsilon [0 degrees, 45 degrees ] is the target polarization angle.
Of the five Kennaugh-Huynen decomposition parameters, the physical significance of m, ψ, and τ is relatively clear: 4 pi m2The maximum scattering sectional area of the target is obtained; the direction angle psi is closely related to the geometric topology of the target and has been successfully used for terrain inversion; the elliptical corners τ depict the symmetry and regularity of the target. In contrast, there is some ambiguity in the target jump angle v and the polarization angle γ. For this purpose, on the basis of the Kennaugh-Huynen decomposition, professor Touzi of the remote sensing center Canada proposed in 2007 a symmetric scattering amplitude αsAnd symmetric scattering phase
Figure BDA0002762791440000021
Two parameters, as a substitute for the target jump angle v and the polarization angle γ:
Figure BDA0002762791440000022
this decomposition is called Touzi decomposition, which is also called Kennaugh-Huynen-Touzi decomposition (hereinafter abbreviated as KHT decomposition) in combination with Kennaugh-Huynen decomposition. The study shows that the parameter alphasThe method can be used for effectively describing a target scattering mechanism, and has good consistency with the famous Cloude-Pottier alpha angle parameter on the scattering of a symmetric target; and symmetric scattering phase
Figure BDA0002762791440000023
Has excellent table in wetland classificationNow. These have led to KHT decomposition which has gained widespread interest and use in recent years.
Nevertheless, from a matrix transformation point of view, the KHT decomposition is not a unitary transformation in the traditional sense, but a co-similarity transformation, which makes it difficult to solve directly. Thus, a currently common method is to construct a Graves matrix [ G ]
Figure BDA0002762791440000024
Wherein
[GD(m，γ)]＝[SD(m，γ)]*[SD(m，γ)]
Thereby will be to the scattering matrix S]The co-similarity transformation of (G) is converted into a pair of Graves matrices [ G ]]Second order unitary transformation of: at this time [ U ]2(υ，τ，ψ)]And [ G ]D(m，γ)]Are respectively a matrix [ G]The eigenvector matrix and eigenvalue matrix of (2) may be implemented by eigen decomposition. Then by decomposing [ U ]2(υ，τ，ψ)]Extracting the parameters psi, tau and upsilon according to the parameters; by decomposition of [ GD(m，γ)]The extraction of the parameters m and gamma is realized; passing parameter alpha againsAnd
Figure BDA0002762791440000025
the nonlinear conversion relation between the parameters upsilon and gamma is realized to the parameter alphasAnd
Figure BDA0002762791440000026
and (4) final extraction.
As can be seen from the above description, although the matrix G]Is constructed to have KHT parameters phi, tau, upsilon, m, gamma and alphasAnd
Figure BDA0002762791440000027
the solution of (2) is possible, but the serial parameter extraction process is still very complicated and cannot be realized in parallel. This will bring great computational pressure on the high resolution big data polarized images faced by current and future polarized radar remote sensing.
To solve this problem, the co-similarity transform to the target scattering matrix [ S ] can be further equivalently transformed into a third order unitary transform to a 3 × 3 target coherence matrix [ T ] using the transform relationship of the polarized SU (2) constellation to SU (3) constellation:
Figure BDA0002762791440000031
wherein
Figure BDA0002762791440000032
In the formula, k is Pauli vector; a. the0、B0B, C, D, E, F, G and H are called Huynen parameters, which are related to the scattering matrix element SHH、SHVAnd SVVThe relationship between (A) and (B) is as follows:
Figure BDA0002762791440000033
unitary matrix U3(ψ，τ，υ)]Further composed of three matrices belonging to the third order special unitary group SU (3):
[U3(ψ，τ，υ)]＝[U3(ψ)][U3(τ)][U3(υ)]
here, the
Figure BDA0002762791440000034
Wherein [ U ]3(ψ)]、[U3(τ)]And [ U ]3(υ)]Are respectively and [ U2(ψ)]、[U2(τ)]And [ U ]2(υ)]The corresponding SU (3) polarization rotation matrix, SU (3) polarization ellipse matrix, and SU (3) polarization phase matrix.
Matrix [ T ]D(m，γ)]Comprises the following steps:
Figure BDA0002762791440000035
using the above transformation relationships, a number of rigorous derivations can be derived:
Figure BDA0002762791440000041
further using the parameter alphasAnd
Figure BDA0002762791440000042
the nonlinear conversion relationship between the parameters upsilon and gamma can be obtained as follows:
Figure BDA0002762791440000043
the above two formulas show all KHT parameters psi, tau, upsilon, m, gamma and alpha for the first timesAnd
Figure BDA0002762791440000044
the analytical solution of (2). Therefore, by scattering the matrix [ S ] to the target]Is converted into a pair-target coherence matrix [ T ]]The third-order unitary transformation is based on the target Huynen parameter, and all KHT parameters can be directly and parallelly solved by utilizing the above two formulas. The calculation process does not need any matrix transformation and decomposition operation, and the method has important application value for target identification and interpretation based on the high-resolution big data polarized image.
Disclosure of Invention
The invention aims to provide all KHT parameters psi, tau, upsilon, m, gamma and alpha for the first time by utilizing the equivalence relation between 2 multiplied by 2 target scattering matrix common similarity transformation and 3 multiplied by 3 target coherent matrix unitary transformationsAnd
Figure BDA0002762791440000045
the analytical expression of (2) provides a rapid implementation method of polarization KHT decomposition.
Embodiment 1 of the present invention provides a method for rapidly implementing polarized KHT decomposition, including:
reading in data of a polarization scattering matrix [ S ] to be decomposed, and calculating Huynen parameters;
calculating Kennaugh-Huynen decomposition parameters based on the Huynen parameters, the Kennaugh-Huynen decomposition parameters comprising: the method comprises the following steps of (1) obtaining a target maximum scattering amplitude m, an orientation angle psi, a helical angle tau, a polarization angle gamma and a jump angle upsilon;
calculating Touzi decomposition parameters based on the Huynen parameters, wherein the Touzi decomposition parameters comprise: symmetric scattering amplitude alphasAnd symmetric scattering phase
Figure BDA0002762791440000046
As an improvement of the method, reading data of a polarization scattering matrix [ S ] to be decomposed, and calculating a Huynen parameter; the method specifically comprises the following steps:
reading in a polarization scattering matrix [ S ] to be decomposed into:
Figure BDA0002762791440000051
wherein S isHVRepresenting a target scattering coefficient received by the horizontal polarization antenna after the vertical polarization antenna transmits electromagnetic waves to irradiate the target; sHHRepresenting a scattering coefficient of a target received by the horizontal polarization antenna after the horizontal polarization antenna transmits electromagnetic waves to irradiate the target; sVVRepresenting a scattering coefficient of a target received by the vertical polarization antenna after the vertical polarization antenna transmits electromagnetic waves to irradiate the target; sVHRepresenting a target scattering coefficient received by the vertical polarization antenna after the horizontal polarization antenna transmits electromagnetic waves to irradiate the target; under the condition of single-station radar backscattering, the target scattering satisfies the dissimilarity: sHV＝SVH；
Based on scattering matrix elements SHH、SHVAnd SVVFive Huynen parameters A0、B0The calculation formulas for C, F and H are as follows:
Figure BDA0002762791440000052
Figure BDA0002762791440000053
Figure BDA0002762791440000054
Figure BDA0002762791440000055
Figure BDA0002762791440000056
wherein Re {. and Im {. can represent the real and imaginary operations, respectively.
As an improvement of the above method, the calculation of the Kennaugh-Huynen decomposition parameter based on the Huynen parameter; the method specifically comprises the following steps:
Figure BDA0002762791440000057
as an improvement of the above method, the calculation of the Touzi decomposition parameter based on the Huynen parameter; the method specifically comprises the following steps:
Figure BDA0002762791440000061
embodiment 2 of the present invention provides a system for rapidly implementing polarization KHT decomposition, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The invention has the advantages that:
the method and system of the present invention utilizes a 2 x 2 target scattering matrix co-similarity transformation andthe equivalence relation between 3 x 3 target coherent matrix unitary transformations gives all KHT parameters psi, tau, upsilon, m, gamma and alpha for the first timesAnd
Figure BDA0002762791440000062
the analytical expression of (2) enables fast implementation of polarization KHT decomposition.
Drawings
FIG. 1 is a general flow chart of the method for the rapid implementation of polarized KHT decomposition according to the present invention;
FIG. 2 is a flow chart showing the method for rapidly realizing polarized KHT decomposition according to the present invention;
FIG. 3 is a graph of total scattered power of a polarized radar image to be decomposed as used in one example;
FIG. 4 is a Huynen parameter A extracted based on a polarization radar image in an example0；
FIG. 5 is a Huynen parameter B extracted based on a polarization radar image in an example0；
FIG. 6 is a Huynen parameter C extracted based on the polarization radar image in the example;
FIG. 7 is a Huynen parameter F extracted based on the polarization radar image in the example;
FIG. 8 is a Huynen parameter H extracted based on the polarization radar image in the example;
FIG. 9 is a graph of the maximum scattering amplitude m of the target obtained by decomposing the image of the polarimetric radar in the example by the method of the present invention;
FIG. 10 is a target orientation angle ψ of a polarized radar image after decomposition by the method of the present invention in an example;
FIG. 11 is a graph of a target pitch angle τ of an exemplary polarized radar image after decomposition by a method of the present invention;
FIG. 12 is a target polarization angle γ of a polarized radar image obtained by decomposing the image by the method of the present invention in an example;
FIG. 13 is a target jump angle upsilon of a polarized radar image of an example after decomposition by the method of the present invention;
FIG. 14 shows the symmetric scattering amplitude α of the polarimetric radar image obtained by the decomposition method of the present invention in the examples；
FIG. 15 shows the symmetric scattering phases of the polarimetric radar image of the example after decomposition by the method of the present invention
Figure BDA0002762791440000063
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
Referring to fig. 1, embodiment 1 of the present invention proposes a method for rapidly implementing polarized KHT decomposition, including the following steps:
step 1) reading in a polarization scattering matrix [ S ] to be decomposed]Data, calculation of Huynen parameter A0、B0C, F and H;
step 2) based on the Huynen parameter A0、B0C, F and H calculate Kennaugh-Huynen decomposition parameters such as maximum scattering amplitude m, orientation angle psi, helical angle tau, polarization angle gamma and jump angle upsilon of target and symmetric scattering amplitude alphasAnd symmetric scattering phase
Figure BDA0002762791440000071
And the Touzi decomposition parameters are equal.
The steps in the method of the present invention are further described below with reference to fig. 2.
In step 1), a polarization scattering matrix [ S ] to be decomposed is read in]Data, calculation A0、B0The Huynen parameters of C, F and H. In one embodiment, the read-in polarized radar image scattering matrix S to be decomposed]Total scattered power (i.e. | S) of the dataHH|2+|SVV|2+2|SHV|2) As shown in fig. 3, the image size is 2000 × 2000, and is acquired by the japanese airborne total polarization interferometric radar system Pi-SAR in the japanese wave building region. If the read-in target scatters 2X 2 matrix S]Is composed of
Figure BDA0002762791440000072
Wherein S isHVRepresenting the emission of electromagnetic radiation by a vertically polarised (V) antennaAfter the target is irradiated, horizontally polarizing (H) the scattering coefficient of the target received by the antenna; sHHRepresenting a scattering coefficient of a target received by the horizontal polarization antenna after the horizontal polarization antenna transmits electromagnetic waves to irradiate the target; sVVRepresenting a scattering coefficient of a target received by the vertical polarization antenna after the vertical polarization antenna transmits electromagnetic waves to irradiate the target; sVHRepresenting the scattering coefficient of the target received by the vertical polarization antenna after the horizontal polarization antenna transmits the electromagnetic wave to irradiate the target.
Under the condition of single-station radar backscattering, the target scattering satisfies the dissimilarity: sHV＝SVH. Based on scattering matrix elements SHH、SHVAnd SVVCalculating A by the following equation0、B0Huynen parameters, C, F and H:
Figure BDA0002762791440000073
wherein Re {. and Im {. can represent the real and imaginary operations, respectively. FIGS. 4-8 show the Huynen parameter A extracted from the polarization radar image according to the embodiment0、B0C, F and H.
Based on the Huynen parameter A extracted in the step 1)0、B0C, F and H, in step 2) we further perform the following:
step 2-1) calculating Kennaugh-Huynen decomposition parameters m, ψ, τ, γ, and ν:
Figure BDA0002762791440000081
fig. 9-13 show the Kennaugh-Huynen decomposition parameters such as the maximum scattering amplitude m, the orientation angle psi, the helix angle tau, the polarization angle gamma, and the jump angle upsilon of the target obtained by decomposing the polarized radar image by the method of the invention in the embodiment.
Step 2-2) calculating the symmetric scattering amplitude alphasAnd symmetric scattering phase
Figure BDA0002762791440000082
And (3) waiting for Touzi decomposition parameters:
Figure BDA0002762791440000083
FIGS. 14 and 15 are graphs showing the symmetric scattering amplitude α obtained by decomposing the polarization radar image according to the method of the present invention in the examplesAnd symmetric scattering phase
Figure BDA0002762791440000084
And the Touzi decomposition parameters are equal.
Embodiment 2 of the present invention provides a system for rapidly implementing polarization KHT decomposition, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of embodiment 1 when executing the computer program.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (5)

1. A method for the rapid implementation of polarized KHT decomposition, said method comprising:
reading in data of a polarization scattering matrix [ S ] to be decomposed, and calculating Huynen parameters;
calculating Kennaugh-Huynen decomposition parameters based on the Huynen parameters, the Kennaugh-Huynen decomposition parameters comprising: the method comprises the following steps of (1) obtaining a target maximum scattering amplitude m, an orientation angle psi, a helical angle tau, a polarization angle gamma and a jump angle upsilon;
calculating Touzi decomposition parameters based on the Huynen parameters, wherein the Touzi decomposition parameters comprise: symmetric scattering amplitude alphasAnd symmetric scattering phase
Figure FDA0002762791430000011
2. The method for rapidly realizing polarization KHT decomposition according to claim 1, characterized in that said method reads in the data of the polarization scattering matrix [ S ] to be decomposed, calculates the Huynen parameter; the method specifically comprises the following steps:
reading in a polarization scattering matrix [ S ] to be decomposed into:
Figure FDA0002762791430000012
wherein S isHVRepresenting a target scattering coefficient received by the horizontal polarization antenna after the vertical polarization antenna transmits electromagnetic waves to irradiate the target; sHHRepresenting a scattering coefficient of a target received by the horizontal polarization antenna after the horizontal polarization antenna transmits electromagnetic waves to irradiate the target; sVVRepresenting a scattering coefficient of a target received by the vertical polarization antenna after the vertical polarization antenna transmits electromagnetic waves to irradiate the target; sVHRepresenting a target scattering coefficient received by the vertical polarization antenna after the horizontal polarization antenna transmits electromagnetic waves to irradiate the target; under the condition of single-station radar backscattering, the target scattering satisfies the dissimilarity: sHV＝SVH；
Based on scattering matrix elements SHH、SHVAnd SVVFive Huynen parameters A0、B0The calculation formulas for C, F and H are as follows:
Figure FDA0002762791430000013
Figure FDA0002762791430000014
Figure FDA0002762791430000015
Figure FDA0002762791430000016
Figure FDA0002762791430000017
wherein Re {. and Im {. can represent the real and imaginary operations, respectively.
3. Method for the rapid realization of polarization KHT decomposition according to claim 1, characterized in that said calculation of the Kennaugh-Huynen decomposition parameters based on Huynen parameters; the method specifically comprises the following steps:
Figure FDA0002762791430000021
4. method for the rapid implementation of a polarization KHT decomposition according to claim 1, characterized in that said calculation of the Touzi decomposition parameters is based on Huynen parameters; the method specifically comprises the following steps:
Figure FDA0002762791430000022
5. a fast implementation system of polarization KHT decomposition, comprising a memory, a processor and a computer program stored on said memory and executable on said processor, characterized in that said processor implements the method according to any of claims 1-4 when executing said computer program.
CN202011223258.XA 2020-11-05 2020-11-05 Method and system for rapidly realizing polarization KHT decomposition Active CN112597434B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011223258.XA CN112597434B (en) 2020-11-05 2020-11-05 Method and system for rapidly realizing polarization KHT decomposition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011223258.XA CN112597434B (en) 2020-11-05 2020-11-05 Method and system for rapidly realizing polarization KHT decomposition

Publications (2)

Publication Number Publication Date
CN112597434A true CN112597434A (en) 2021-04-02
CN112597434B CN112597434B (en) 2024-03-22

Family

ID=75180582

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011223258.XA Active CN112597434B (en) 2020-11-05 2020-11-05 Method and system for rapidly realizing polarization KHT decomposition

Country Status (1)

Country Link
CN (1) CN112597434B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102262223A (en) * 2010-05-26 2011-11-30 中国科学院对地观测与数字地球科学中心 Method for decomposing coherent targets based on scattering matrixes
CN104459656A (en) * 2014-12-31 2015-03-25 中国科学院空间科学与应用研究中心 Target orientation angle compensation method for fully polarimetric synthetic aperture radar
CN104931950A (en) * 2015-06-23 2015-09-23 中国科学院空间科学与应用研究中心 Target decomposition method based on model for fully-polarized synthetic aperture radar
CN109375189A (en) * 2018-12-25 2019-02-22 杭州世平信息科技有限公司 Polarimetric radar remote sensing images city goal decomposition method based on cross scatter model
CN109633583A (en) * 2018-10-22 2019-04-16 上海无线电设备研究所 The target shape inversion method of Surface scan Polarization scattering data is surrounded based near field
CN109740109A (en) * 2018-12-25 2019-05-10 中国科学院国家空间科学中心 A kind of PolSAR image broad object decomposition method based on unitary transformation
CN109753905A (en) * 2018-12-25 2019-05-14 中国科学院国家空间科学中心 A kind of polarimetric SAR image goal decomposition method based on expansion G4U
CN111025291A (en) * 2019-11-06 2020-04-17 中国石油大学(华东) Ocean oil spill detection method based on new characteristics of fully-polarized SAR

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102262223A (en) * 2010-05-26 2011-11-30 中国科学院对地观测与数字地球科学中心 Method for decomposing coherent targets based on scattering matrixes
CN104459656A (en) * 2014-12-31 2015-03-25 中国科学院空间科学与应用研究中心 Target orientation angle compensation method for fully polarimetric synthetic aperture radar
CN104931950A (en) * 2015-06-23 2015-09-23 中国科学院空间科学与应用研究中心 Target decomposition method based on model for fully-polarized synthetic aperture radar
CN109633583A (en) * 2018-10-22 2019-04-16 上海无线电设备研究所 The target shape inversion method of Surface scan Polarization scattering data is surrounded based near field
CN109375189A (en) * 2018-12-25 2019-02-22 杭州世平信息科技有限公司 Polarimetric radar remote sensing images city goal decomposition method based on cross scatter model
CN109740109A (en) * 2018-12-25 2019-05-10 中国科学院国家空间科学中心 A kind of PolSAR image broad object decomposition method based on unitary transformation
CN109753905A (en) * 2018-12-25 2019-05-14 中国科学院国家空间科学中心 A kind of polarimetric SAR image goal decomposition method based on expansion G4U
CN111025291A (en) * 2019-11-06 2020-04-17 中国石油大学(华东) Ocean oil spill detection method based on new characteristics of fully-polarized SAR

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
DONG LI 等: "A mathematical extension to the general four-component scattering power decomposition with unitary transformation of coherency matrix", IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 58, no. 11, 27 April 2020 (2020-04-27), pages 7772 - 7789, XP011816659, DOI: 10.1109/TGRS.2020.2983758 *
DONG LI 等: "Monitoring of tsunami/eaqthquake damages by polarimetric microwave remote sensing technique", TSUNAMI—DAMAGE ASSESSMENT MEDICAL TRIAGE, 20 February 2020 (2020-02-20) *
LITING LIANG 等: "Fast Huynen–Euler Decomposition and its Application in Disaster Monitoring", IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, no. 4, 5 April 2021 (2021-04-05), pages 4231 - 4243, XP011852111, DOI: 10.1109/JSTARS.2021.3070897 *
朱飞亚: "全极化合成孔径雷达信息预处理和目标分解方法研究", 中国博士学位论文全文数据库 信息科技辑, 15 September 2017 (2017-09-15), pages 136 - 60 *
梁莉婷: "基于特殊酉群的极化合成孔径雷达信息提取与应用", 中国博士学位论文全文数据库 信息科技辑, 15 January 2022 (2022-01-15), pages 136 - 284 *

Also Published As

Publication number Publication date
CN112597434B (en) 2024-03-22

Similar Documents

Publication Publication Date Title
Yin et al. Simultaneous image fusion and super-resolution using sparse representation
Yang et al. Robust zero watermarking algorithm for medical images based on zernike-DCT
Deeba et al. Lossless digital image watermarking in sparse domain by using K‐singular value decomposition algorithm
Luo et al. IMNN-LWEC: A novel infrared small target detection based on spatial–temporal tensor model
Nikvand et al. Generic image similarity based on Kolmogorov complexity
Xian et al. Super-resolved fine-scale sea ice motion tracking
Zhang et al. RST resilient watermarking scheme based on DWT-SVD and scale-invariant feature transform
Tian et al. A novel mosaic method for spaceborne ScanSAR images based on homography matrix compensation
CN112597434A (en) Rapid implementation method and system for polarized KHT decomposition
Li et al. Semi-supervised implicit neural representation for polarimetric ISAR image super-resolution
Wang et al. CVAE: An efficient and flexible approach for sparse aperture ISAR imaging
Xu et al. A fast mixing matrix estimation method in the wavelet domain
Tong et al. Dictionary learning for medical image denoising, reconstruction, and segmentation
Yang et al. A hyper-chaotically encrypted robust digital image watermarking method with large capacity using compress sensing on a hybrid domain
Wang et al. A Zero-Watermark Scheme Based on Quaternion Generalized Fourier Descriptor for Multiple Images.
Dou et al. The efficient norm regularization method applying on the ISAR image with sparse data
Titin-Schnaider Characterization and recognition of bistatic polarimetric mechanisms
Daoui et al. Robust 2D and 3D images zero-watermarking using dual Hahn moment invariants and Sine Cosine Algorithm
Chen et al. PCNN-based image fusion in compressed domain
Xian-chuan et al. Remote sensing image fusion based on integer wavelet transformation and ordered nonnegative independent component analysis
Gou et al. Image super‐resolution based on the pairwise dictionary selected learning and improved bilateral regularisation
Han et al. Medical image watermarking in sub-block three-dimensional discrete cosine transform domain
Yang et al. An accurate registration method based on global mixed structure similarity (GMSIM) for remote sensing images
Wu et al. Fast two‐dimensional sparse signal gridless recovery algorithm for MIMO array SAR 3D imaging
Zhu et al. A novel image super-resolution reconstruction method based on sparse representation using classified dictionaries

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant