Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/164935
Title: A New Algorithm for Optimizing TV-Based PolSAR Despeckling Model
Authors: Xiangli Nie;Bo Zhang;Yunjin Chen;Hong Qiao
subject: Nonconvex optimization|polarimetric synthetic aperture radar (PolSAR)|variational method
Year: 2016
Publisher: IEEE
Abstract: The Wishart fidelity and total variation (TV) based variational model (WisTV) with the positive definite (PD) constraint has shown to be effective for the whole PolSAR covariance data speckle reduction. However, the existing algorithms for solving the WisTV model only give approximation solutions by projecting the results onto the set of PD matrices, and their parameters depend strongly on the data. The purpose of this letter is to propose a new optimization algorithm to address the issues. To keep the uniformity of the parameters for different PolSAR data, a sigmoid function-based normalization method is designed, which ensures the applicability of the WisTV model for the normalized data. By using the orthogonal decomposition of the PD variables, the WisTV model is converted into an unconstrained optimization problem which is further transformed into a multivariable problem based on the equivalent representations of the trace and logdet functions. The alternative minimization technique is then utilized to solve the final optimization problem. The subproblems for each individual variable are convex and their solutions have explicit expressions. Moreover, the computational complexity of the algorithm is discussed. Experimental results on both synthetic and real PolSAR data demonstrate the validity of the proposed algorithm.
URI: http://localhost/handle/Hannan/164935
ISSN: 1070-9908
1558-2361
volume: 23
issue: 10
More Information: 1409
1413
Appears in Collections:2016

Files in This Item:
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Title: A New Algorithm for Optimizing TV-Based PolSAR Despeckling Model
Authors: Xiangli Nie;Bo Zhang;Yunjin Chen;Hong Qiao
subject: Nonconvex optimization|polarimetric synthetic aperture radar (PolSAR)|variational method
Year: 2016
Publisher: IEEE
Abstract: The Wishart fidelity and total variation (TV) based variational model (WisTV) with the positive definite (PD) constraint has shown to be effective for the whole PolSAR covariance data speckle reduction. However, the existing algorithms for solving the WisTV model only give approximation solutions by projecting the results onto the set of PD matrices, and their parameters depend strongly on the data. The purpose of this letter is to propose a new optimization algorithm to address the issues. To keep the uniformity of the parameters for different PolSAR data, a sigmoid function-based normalization method is designed, which ensures the applicability of the WisTV model for the normalized data. By using the orthogonal decomposition of the PD variables, the WisTV model is converted into an unconstrained optimization problem which is further transformed into a multivariable problem based on the equivalent representations of the trace and logdet functions. The alternative minimization technique is then utilized to solve the final optimization problem. The subproblems for each individual variable are convex and their solutions have explicit expressions. Moreover, the computational complexity of the algorithm is discussed. Experimental results on both synthetic and real PolSAR data demonstrate the validity of the proposed algorithm.
URI: http://localhost/handle/Hannan/164935
ISSN: 1070-9908
1558-2361
volume: 23
issue: 10
More Information: 1409
1413
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7551150.pdf391.88 kBAdobe PDFThumbnail
Preview File
Title: A New Algorithm for Optimizing TV-Based PolSAR Despeckling Model
Authors: Xiangli Nie;Bo Zhang;Yunjin Chen;Hong Qiao
subject: Nonconvex optimization|polarimetric synthetic aperture radar (PolSAR)|variational method
Year: 2016
Publisher: IEEE
Abstract: The Wishart fidelity and total variation (TV) based variational model (WisTV) with the positive definite (PD) constraint has shown to be effective for the whole PolSAR covariance data speckle reduction. However, the existing algorithms for solving the WisTV model only give approximation solutions by projecting the results onto the set of PD matrices, and their parameters depend strongly on the data. The purpose of this letter is to propose a new optimization algorithm to address the issues. To keep the uniformity of the parameters for different PolSAR data, a sigmoid function-based normalization method is designed, which ensures the applicability of the WisTV model for the normalized data. By using the orthogonal decomposition of the PD variables, the WisTV model is converted into an unconstrained optimization problem which is further transformed into a multivariable problem based on the equivalent representations of the trace and logdet functions. The alternative minimization technique is then utilized to solve the final optimization problem. The subproblems for each individual variable are convex and their solutions have explicit expressions. Moreover, the computational complexity of the algorithm is discussed. Experimental results on both synthetic and real PolSAR data demonstrate the validity of the proposed algorithm.
URI: http://localhost/handle/Hannan/164935
ISSN: 1070-9908
1558-2361
volume: 23
issue: 10
More Information: 1409
1413
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7551150.pdf391.88 kBAdobe PDFThumbnail
Preview File