CN108830799A - Polarization SAR image speckle suppression method based on opposite polarisation total variation - Google Patents

Polarization SAR image speckle suppression method based on opposite polarisation total variation Download PDF

Info

Publication number
CN108830799A
CN108830799A CN201810413022.9A CN201810413022A CN108830799A CN 108830799 A CN108830799 A CN 108830799A CN 201810413022 A CN201810413022 A CN 201810413022A CN 108830799 A CN108830799 A CN 108830799A
Authority
CN
China
Prior art keywords
polarization
elements
total variation
main diagonal
matrix
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
CN201810413022.9A
Other languages
Chinese (zh)
Other versions
CN108830799B (en
Inventor
江畅
李文梅
陈祥
陈一祥
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.)
Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing Post and Telecommunication University
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 Nanjing Post and Telecommunication University filed Critical Nanjing Post and Telecommunication University
Priority to CN201810413022.9A priority Critical patent/CN108830799B/en
Publication of CN108830799A publication Critical patent/CN108830799A/en
Application granted granted Critical
Publication of CN108830799B publication Critical patent/CN108830799B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • 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
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/904—SAR modes
    • G01S13/9076—Polarimetric features in SAR
    • 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
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Theoretical Computer Science (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The polarization SAR image speckle suppression method based on opposite polarisation total variation that the present invention relates to a kind of, the elements in a main diagonal and off diagonal element are separated by the polarization coherence matrix to polarization SAR image, application widget convolution part total variation and global total variation update the elements in a main diagonal, by former off diagonal element and updated the elements in a main diagonal reconstruct polarization coherence matrix, polarization coherence matrix distance using the polarization coherence matrix ratio in adjacent picture elements and before and after updating, obtains the pixel position that must retain polarization characteristic.The present invention only carries out Speckle reduction to the elements in a main diagonal for the polarization coherence matrix for being not belonging to above-mentioned pixel position, it solves the problems, such as well combine both the structural information of polarization SAR image and scattering properties in existing polarization SAR Speckle reduction and carries out Speckle reduction, so that the Speckle reduction of polarization SAR is all maintained in structural information and two aspect of scattering properties.

Description

Polarization SAR image speckle suppression method based on opposite polarisation total variation
Technical field
The polarization SAR image speckle suppression method based on opposite polarisation total variation that the present invention relates to a kind of, belongs to full pole Change SAR (Synthetic Aperture Radar, synthetic aperture radar) remote sensing technology field.
Background technique
Polarization SAR is a kind of for measuring the imaging radar of radiation signal polarization characteristic, can obtain different polarized states Under target scattering feature.Therefore, polarization SAR is widely used in the fields such as agricultural investigation, forestry research and geological prospecting.So And since polarization SAR has the coherent speckle noise being largely in granular form, cause image interpretation unobvious, or even seriously affect The application of image.Therefore, Speckle reduction is carried out to polarization SAR image to have great importance.
Polarization SAR image speckle suppression method is broadly divided into three classes at present：The first kind is based on adaptive coherent spot Inhibit, the second class is the Speckle reduction based on non-local mean method, and third class is the coherent spot suppression based on total variation method System.
It is based primarily upon Minimum Mean Square Error error based on adaptive speckle suppression method, by adjusting template window drawn game The method of portion's statistical value reduces coherent spot, such as Lee filtering, Kuan filtering, Frost filtering.In order to preferably retain edge details, The method that many sides are inhibited based on adaptive coherent spot is proposed in succession.Have and proposes within researcher 2014 a kind of adaptive enhancing Lee filtering method promotes phase by increasing by one group of uniform window and one group of linear direction window and adaptively adjusting window size Dry spot inhibitory effect.However, based on adaptive speckle suppression method needing that more ginseng is arranged for window adaptation mechanism Number causes calculating process time-consuming, and operational efficiency is low.
Speckle suppression method based on non-local mean is weighted average filtering method, is mainly made using image similar block For the weighting weight of filtering, to retain structure feature.This method has received the extensive pass of numerous researchers since proposition Note.Have propose non local weighted least mean square difference filtering method within researcher 2015, theoretical using non-local mean obtains most The weight of pixel samples in small mean square deviation method.However, such methods can generally make the edge and details letter of image in denoising Breath blurring, and operand is larger.
In speckle suppression method based on total variation method, regular terms is substantially a kind of local filter, regularization State modulator the inhibition level of noise.Have propose within researcher 2014 it is a kind of based on the non-convex of spatially adaptive regularization parameter Regular terms total variation speckle suppression method is utilized using adaptive regularization state modulator different zones noise suppressed degree Non-convex regular terms keeps image edge and texture detail information.Have propose a kind of coherent spot based on total variation within researcher 2017 Suppressing method converts additive noise for the multiplicative noise of SAR by logarithmic transformation, then applies total variation Speckle reduction. However, coherent spot affects the correlation of each POLARIZATION CHANNEL and interchannel, and target scattering is special in polarization SAR image Sign is contained in covariance matrix or polarization coherence matrix.
Summary of the invention
It is an object of the invention to：In view of the defects existing in the prior art, it proposes a kind of based on opposite polarisation total variation Polarization SAR image speckle suppression method, iteration remain larger than the coherence matrix that polarizes in the pixel of a preceding Wishart distance, The leading diagonal member for the coherence matrix that polarizes in the pixel for being less than a preceding Wishart distance is updated using opposite total variation method Element realizes polarization SAR image Speckle reduction, while can retain polarization characteristic and architectural characteristic.
To achieve the above object, the technical scheme is that：The phase of opposite polarisation total variation is carried out to polarization SAR image Dry spot inhibits, and includes the following steps：
Step 1, Speckle reduction is carried out to polarization SAR image, the polarization coherence matrix of polarization SAR data is divided From being divided into the elements in a main diagonal and off diagonal element two parts；
Step 1.1, the polarization phase for obtaining polarization SAR data is read from polarization SAR data using polarization SAR processing software Dry matrix；
Step 1.2, polarization coherence matrix T is expressed as：
Wherein, SHHIndicate H to transmitting and H to received echo data, SHVIndicate H to transmitting and V to received number of echoes According to SVVV is indicated to transmitting and V to received echo data, * indicates complex conjugate transposition,With 2 | SHV |2Respectively indicate T11, T22 and the T33 of polarization coherence matrix T；
Step 1.3, polarization coherence matrix T is separated into 3 the elements in a main diagonal and 6 off diagonal elements.
Step 2, Speckle reduction is carried out for the elements in a main diagonal, application widget convolution total variation and whole picture image become entirely Difference updates the elements in a main diagonal；
Step 2.1, the elements in a main diagonal is calculated in first derivative L both horizontally and verticallyxAnd Ly, calculate weight
Step 2.2, it calculates the elements in a main diagonal and two secondary volumes is carried out with Gassian low-pass filter with the window of [1, round (5 σ)] Product operation, enables σ=0.5, is acquired on both horizontally and vertically respectively to the elements in a main diagonal obtained after convolution algorithm and seek single order Derivative DxAnd Dy, calculate separately weightWithTo obtain the weight W on both horizontally and verticallyx =W1·W2,xAnd Wy=W1·W2,y；
Step 2.3, the initial minimum of the elements in a main diagonal is calculatedWithMinimizing equation is
Wherein, u represents T3Former the elements in a main diagonal；It is the gradient of u, λ is Lagrange multiplier, optimal value 0.002；
It results in updatedThe elements in a main diagonalWithDxAnd DyIt is water in window respectively The total variation of flat vertical direction, LxAnd LyTotal variation both horizontally and vertically respectively.
Step 3, reconstruct polarization coherence matrix, by calculating the matrix ratio R atio between obtaining adjacent picture elementsi,jAnd update The polarization coherence matrix distance D of front and back(k), calculate the ranks number for retaining polarization characteristic：
FoundationWithObtain line number I=I1I I2(i1∈I1,i2∈I2) and row number J=J1I J2(j1∈J1,j2∈J2)；
Step 3.1, updated the elements in a main diagonal and off diagonal element the reconstruct polarization phase obtained in step 2 is utilized Dry matrix T；
Step 3.2, the matrix ratio R atio between adjacent picture elements is calculatedi,j：
Wherein, T3',xAnd T3',yBe respectively both horizontally and vertically on first derivative, conj () expression seek matrix Complex conjugate transposition；
Step 3.3, the polarization coherence matrix distance D for updating front and back is calculated(k)：
D(k)=log | T3 (k)|+trace(inv(T3 (k))·T3 (k-1))(k≥1)
Wherein, inv () indicates that finding the inverse matrix, trace () indicate to seek the mark of matrix, | | expression asks matrix to correspond to row The value of column, k indicate the number of iterations；
Step 3.4, satisfaction is soughtWithLine number I=I1 I I2(i1∈I1,i2∈I2) and column Number J=J1 I J2(j1∈J1,j2∈J2)。
Step 4, the opposite total variation of updated the elements in a main diagonal is calculated；
Step 4.1, the elements in a main diagonal for being not involved in update is obtained：T (m, m, i, j)=0 (m ∈ { 1,2,3 })；
Step 4.2, the elements in a main diagonal for participating in updating is calculated with respect to total variation, solves the elements in a main diagonal With
Step 4.3, according to update front and backWithPolarization range formula is calculated, formula is as follows：
D(k)=log | T3 (k)|+trace(inv(T3 (k))·T3 (k-1))(k>0)
Wherein, inv () indicates that finding the inverse matrix, trace () indicate to seek the mark of matrix, and k indicates the number of iterations.
Step 4.4, foundationWithThe pixel line number I=I for being not involved in update obtained1 I I2 (i1∈I1,i2∈I2) and row number J=J1 I J2(j1∈J1,j2∈J2)
The invention adopts the above technical scheme compared with prior art, has the following technical effects：
A) present invention passes through separation the elements in a main diagonal and non-during the Speckle reduction of polarization SAR coherence matrix T Diagonal entry obtains the pixel position for not participating in update by polarization coherence matrix ratio and polarization coherence distance variation, The polarization characteristic of polarization SAR image is thus maintained very well.
B) present invention is during the Speckle reduction of polarization SAR coherence matrix T, due to combining opposite total variation mould Type only solves the elements in a main diagonal and minimizes equation, thus remains the texture and structural characteristic of polarization SAR image.
Detailed description of the invention
The present invention will be further described below with reference to the drawings.
Fig. 1 is flow diagram of the invention.
Fig. 2 is existing polarization raw-data map.
Fig. 3 is the result comparison schematic diagram of the present invention with other speckle suppression methods；
Wherein (a) is original polarization SAR image, is (b) Boxcar filtering Speckle reduction as a result, (c) Refined Lee Speckle reduction result is filtered, is (d) Speckle reduction result of the present invention.
Specific embodiment
A kind of polarization SAR image speckle suppression method based on opposite polarisation total variation is present embodiments provided, is flowed Journey is as shown in Figure 1, specific implementation process is as follows：
(1) the elements in a main diagonal is carried out to the polarization coherence matrix T of polarization SAR data and off diagonal element separates.
1a) using polarization SAR processing software PolSARpro_v5.1.2 from polarization SAR data, reads and obtain polarization SAR The polarization coherence matrix of data；
1b) polarization coherence matrix T is expressed as：
Wherein, SHHIndicate H to transmitting and H to received echo data, SHVIndicate H to transmitting and V to received number of echoes According to SVVV is indicated to transmitting and V to received echo data, * indicates complex conjugate transposition,With 2 | SHV |2Respectively indicate T11, T22 and the T33 of polarization coherence matrix T；
Polarization coherence matrix T 1c) is separated into 3 the elements in a main diagonal and 6 off diagonal elements；
(2) initial minimum of initial distance value and the elements in a main diagonal is calculated；
2a) calculate initial distance value：
D(0)=log | T3 (0)|+trace(inv(T3 (0))·T3 (0))
The elements in a main diagonal 2b) is calculated in first derivative L both horizontally and verticallyxAnd Ly, calculate weight
It 2c) calculates the elements in a main diagonal and secondary convolution fortune is carried out with Gassian low-pass filter with the window of [1, round (5 σ)] It calculates, enables σ=0.5, the elements in a main diagonal obtained after convolution algorithm is acquired respectively on both horizontally and vertically and seeks first derivative DxAnd Dy, calculate separately weightWithTo obtain the weight W on both horizontally and verticallyx= W1·W2,xAnd Wy=W1·W2,y；
2d) calculate the initial minimum of the elements in a main diagonalWithMinimizing equation is
Wherein, u represents T3Former the elements in a main diagonal；It is the gradient of u, λ is Lagrange multiplier, optimal value 0.002.This method has obtained updatedThe elements in a main diagonalWithDxAnd DyIt is horizontal in window respectively The total variation of vertical direction, LxAnd LyTotal variation both horizontally and vertically respectively；
(3) updated polarization coherence matrix T is obtained, the ranks number for retaining polarization characteristic are calculated.
3a) by updated the elements in a main diagonal and off diagonal element reconstruct polarization coherence matrix T；
3b) calculate the matrix ratio R atio between adjacent picture elementsi,jWith the standard deviation △ of normalization Ratio'：
Wherein, T3',xAnd T3',yBe respectively both horizontally and vertically on first derivative, conj () expression seek matrix Complex conjugate transposition；
3c) calculate the polarization coherence matrix distance D for updating front and back(k)：
D(k)=log | T3 (k)|+trace(inv(T3 (k))·T3 (k-1))(k≥1)
Wherein, inv () indicates that finding the inverse matrix, trace () indicate to seek the mark of matrix, | | expression asks matrix to correspond to row The value of column, k indicate the number of iterations；
3d) seek satisfactionWithLine number I=I1 I I2(i1∈I1,i2∈I2) and row number J= J1 I J2(j1∈J1,j2∈J2)；
(4) method based on opposite total variation solves the elements in a main diagonalWith
(5) according to update front and backWithCalculate polarization coherence matrix distance D(k)Matrix between adjacent picture elements Ratio R atioi,j, calculate and meetWithPixel to be updated line number and row number.
(6) iteration executes (3)~(5) step, iterated conditional △≤0.01.
(7) iteration executes the step of (2)~(5), the number of iterations k≤4.
(8) the polarization coherence matrix distance using the polarization coherence matrix ratio in adjacent picture elements and before and after updating, obtains The pixel position of polarization characteristic must be retained, and retain required element, output polarization SAR image.
Polarization SAR image speckle suppression method provided by the invention based on opposite polarisation total variation, not only shortens Elapsed time remains the polarization characteristic and structure feature of polarization SAR image while inhibiting coherent spot.Its effect can lead to Following experiment simulation is crossed to further illustrate.
1. experiment condition
(1) experiment simulation environment is:MATLAB 2014b, Intel (R) Core (TM) i7-2640M CPU 2.80GHz；
(2) test data of experiment is as shown in Figure 1, be that the airborne AIRSAR in the polarization SAR data source U.S. is acquired The area SanFrancisco L-band surveys full-polarization SAR data.
2. experiment content and result
With existing Boxcar filtering and Refined Lee filtering method and the method for the present invention to data shown in Fig. 3 (a) into Row experiment, experimental result are as shown in Figure 3, wherein Fig. 3 (b) is Boxcar filtering Speckle reduction as a result, Fig. 3 (c) is Refined Lee filters Speckle reduction result, and Fig. 3 (c) is Speckle reduction result of the present invention.Compared to Boxcar filtering and Refined Lee filtering, the present invention is obvious in heterogeneous region Speckle reduction effect, remains structure feature information.
3. experimental result is evaluated
In order to objectively evaluate the present invention, coherent spot is evaluated by equivalent number (ENL) and structural similarity index (SSIM) Inhibitory effect objectively proves that effect of the invention is best, as shown in table 1.
1 ENL and SSIM evaluation result of table
The foregoing is merely better embodiment of the invention, protection scope of the present invention is not with above embodiment Limit, as long as those of ordinary skill in the art's equivalent modification or variation made by disclosure according to the present invention, should all be included in power In the protection scope recorded in sharp claim.

Claims (6)

1. the polarization SAR image speckle suppression method based on opposite polarisation total variation, it is characterised in that：To polarization SAR image The Speckle reduction for carrying out opposite polarisation total variation, includes the following steps：
Step 1, Speckle reduction is carried out to polarization SAR image, the polarization coherence matrix of polarization SAR data is separated, point For the elements in a main diagonal and off diagonal element two parts；
Step 2, Speckle reduction is carried out for the elements in a main diagonal, application widget convolution total variation and whole picture image total variation are more New the elements in a main diagonal；
Step 3, reconstruct polarization coherence matrix, by calculating the matrix ratio R atio between obtaining adjacent picture elementsi,jBefore and after updating Polarization coherence matrix distance D(k), calculate the ranks number for retaining polarization characteristic：
FoundationWithObtain line number I=I1I I2(i1∈I1,i2∈I2) and row number J=J1I J2(j1 ∈J1,j2∈J2)；
Step 4, the opposite total variation of updated the elements in a main diagonal is calculated.
2. the polarization SAR image speckle suppression method according to claim 1 based on opposite polarisation total variation, feature It is：The step 1 includes the following steps：
Step 1.1, the relevant square of polarization for obtaining polarization SAR data is read from polarization SAR data using polarization SAR processing software Battle array；
Step 1.2, polarization coherence matrix T is expressed as：
Wherein, SHHIndicate H to transmitting and H to received echo data, SHVIndicate H to transmitting and V to received echo data, SVVV is indicated to transmitting and V to received echo data, * indicates complex conjugate transposition,With 2 | SHV|2Point Biao Shi not polarize T11, T22 and the T33 of coherence matrix T；
Step 1.3, polarization coherence matrix T is separated into 3 the elements in a main diagonal and 6 off diagonal elements.
3. the polarization SAR image speckle suppression method according to claim 1 based on opposite polarisation total variation, feature It is：The step 2 includes the following steps：
Step 2.1, the elements in a main diagonal is calculated in first derivative L both horizontally and verticallyxAnd Ly, calculate weight
Step 2.2, it calculates the elements in a main diagonal and secondary convolution fortune is carried out with Gassian low-pass filter with the window of [1, round (5 σ)] It calculates, enables σ=0.5, the elements in a main diagonal obtained after convolution algorithm is acquired respectively on both horizontally and vertically and seeks first derivative DxAnd Dy, calculate separately weightWithTo obtain the weight W on both horizontally and verticallyx= W1·W2,xAnd Wy=W1·W2,y；
Step 2.3, the initial minimum of the elements in a main diagonal is calculatedWithMinimizing equation is
Wherein, u represents T3Former the elements in a main diagonal；It is the gradient of u, λ is Lagrange multiplier；
It results in updatedThe elements in a main diagonalWithDxAnd DyIt is horizontal vertical in window respectively The total variation in direction, LxAnd LyTotal variation both horizontally and vertically respectively.
4. the polarization SAR image speckle suppression method according to claim 3 based on opposite polarisation total variation, feature It is：In the step 2.3, λ is Lagrange multiplier, optimal value 0.002.
5. the polarization SAR image speckle suppression method according to claim 1 based on opposite polarisation total variation, feature It is：The step 3 includes the following steps：
Step 3.1, the relevant square of updated the elements in a main diagonal and off diagonal element reconstruct polarization obtained in step 2 is utilized Battle array T；
Step 3.2, the matrix ratio R atio between adjacent picture elements is calculatedi,j：
Wherein, T '3,xWith T '3,yBe respectively both horizontally and vertically on first derivative, conj () indicate ask matrix it is multiple be total to Yoke transposition；
Step 3.3, the polarization coherence matrix distance D for updating front and back is calculated(k)：
Wherein, inv () indicates that finding the inverse matrix, trace () indicate to seek the mark of matrix, | | expression asks matrix to correspond to determinant Value, k indicate the number of iterations；
Step 3.4, satisfaction is soughtWithLine number I=I1I I2(i1∈I1,i2∈I2) and row number J= J1I J2(j1∈J1,j2∈J2)。
6. the polarization SAR image speckle suppression method according to claim 1 based on opposite polarisation total variation, feature It is：The step 4 includes the following steps：
Step 4.1, the elements in a main diagonal for being not involved in update is obtained：T (m, m, i, j)=0 (m ∈ { 1,2,3 })；
Step 4.2, the elements in a main diagonal for participating in updating is calculated with respect to total variation, solves the elements in a main diagonal With
Step 4.3, according to update front and backWithPolarization range formula is calculated, formula is as follows：
Wherein, inv () indicates that finding the inverse matrix, trace () indicate to seek the mark of matrix, and k indicates the number of iterations.
Step 4.4, foundationWithThe pixel line number I=I for being not involved in update obtained1I I2(i1∈I1, i2∈I2) and row number J=J1I J2(j1∈J1,j2∈J2)。
CN201810413022.9A 2018-05-03 2018-05-03 Polarization SAR image speckle suppression method based on relative polarization total variation Active CN108830799B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810413022.9A CN108830799B (en) 2018-05-03 2018-05-03 Polarization SAR image speckle suppression method based on relative polarization total variation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810413022.9A CN108830799B (en) 2018-05-03 2018-05-03 Polarization SAR image speckle suppression method based on relative polarization total variation

Publications (2)

Publication Number Publication Date
CN108830799A true CN108830799A (en) 2018-11-16
CN108830799B CN108830799B (en) 2022-04-08

Family

ID=64148077

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810413022.9A Active CN108830799B (en) 2018-05-03 2018-05-03 Polarization SAR image speckle suppression method based on relative polarization total variation

Country Status (1)

Country Link
CN (1) CN108830799B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112990166A (en) * 2021-05-19 2021-06-18 北京远鉴信息技术有限公司 Face authenticity identification method and device and electronic equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104166856A (en) * 2014-07-30 2014-11-26 西安电子科技大学 Polarization SAR image classification method based on neighbor propagation clustering and region growing
CN104331707A (en) * 2014-11-02 2015-02-04 西安电子科技大学 Polarized SAR (synthetic aperture radar) image classification method based on depth PCA (principal component analysis) network and SVM (support vector machine)
CN105718957A (en) * 2016-01-26 2016-06-29 西安电子科技大学 Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104166856A (en) * 2014-07-30 2014-11-26 西安电子科技大学 Polarization SAR image classification method based on neighbor propagation clustering and region growing
CN104331707A (en) * 2014-11-02 2015-02-04 西安电子科技大学 Polarized SAR (synthetic aperture radar) image classification method based on depth PCA (principal component analysis) network and SVM (support vector machine)
CN105718957A (en) * 2016-01-26 2016-06-29 西安电子科技大学 Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
RIDHA TOUZI等: "Statistics of the Stokes Parameters and of the Complex Coherence Parameters in One-Look and Multilook Speckle Fields", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 *
行晓黎等: "顾及目标异质性的极化SAR图像非局部均值滤波", 《遥感学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112990166A (en) * 2021-05-19 2021-06-18 北京远鉴信息技术有限公司 Face authenticity identification method and device and electronic equipment
CN112990166B (en) * 2021-05-19 2021-08-24 北京远鉴信息技术有限公司 Face authenticity identification method and device and electronic equipment

Also Published As

Publication number Publication date
CN108830799B (en) 2022-04-08

Similar Documents

Publication Publication Date Title
Jiang et al. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network
CN105718957A (en) Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network
CN108447041B (en) Multi-source image fusion method based on reinforcement learning
CN105205484B (en) Synthetic aperture radar target detection method based on warp wavelet and Wiener filtering
CN111766577B (en) Power transmission line channel tree height inversion method based on three-stage algorithm P wave band
CN107977944A (en) One kind estimation NPP remote sensing image data generation methods
CN110940638B (en) Hyperspectral image sub-pixel level water body boundary detection method and detection system
CN109212631A (en) Satellite observation data three-dimensional variation assimilation method considering channel correlation
Patel et al. K-SVD for Hardi denoising
CN106022383B (en) SAR target identification method based on azimuth associated dynamic dictionary rarefaction representation
Ren et al. A global weighted least-squares optimization framework for speckle filtering of PolSAR imagery
CN108830799A (en) Polarization SAR image speckle suppression method based on opposite polarisation total variation
CN108875798B (en) Super-pixel-level feature extraction method based on spatial pyramid pooling
Luo et al. Despeckling multitemporal polarimetric SAR data based on tensor decomposition
CN107144841B (en) Polarized SAR image target decomposition method based on minimum residual power
Ma et al. PolSAR anisotropic diffusion filter with a refined similarity measure and an adaptive fidelity constraint
CN114004764B (en) Improved sensitivity coding reconstruction method based on sparse transform learning
CN116401889A (en) Cerebellum partitioning method based on functional connection optimization and spectral clustering
Sebastianelli et al. A speckle filter for SAR Sentinel-1 GRD data based on Residual Convolutional Neural Networks
Yufeng et al. Research on SAR image change detection algorithm based on hybrid genetic FCM and image registration
Amao-Oliva et al. The beltrami SAR framework for multichannel despeckling
CN115453533A (en) Time sequence InSAR distributed target phase optimization method
Peng et al. Millimeter wave image super resolution using multichannel depth convolution neural network
CN116797845B (en) Unsupervised reduced polarization classification method based on scattering mechanism
CN117115015B (en) Sea wave suppression method, system, equipment and medium in SAR ocean image

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