Independent Component Analysis of POLSAR Images. Relative Newton-Based Approach

We propose here a new method for POLSAR image analysis. The method is based on a new PCA-ICA model in which the relative Newton-based approach for performing ICA is developed. The basic idea of ICA with relative Newton method consists in approximating the negentropy by taking account of the orthogonality constraint of the extracted components. This concept is recognized for its robustness and gives consequently very good theoretical results. The approach is well justified from the mathematical point of view. However, its implementation requires being more flexible because of the number of the estimated parameters. The purpose of this paper is to try to open new issues, in future research, in the concern of working out a new method for SAR image analysis that accumulate the advantages of the proposed method while avoiding its disadvantages.

[1]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[2]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[3]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[4]  S. Quegan,et al.  Understanding Synthetic Aperture Radar Images , 1998 .

[5]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

[6]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[7]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[8]  Noboru Murata,et al.  Multiplicative Nonholonomic/Newton -like Algorithm , 2000, ArXiv.

[9]  Dinh Tuan Pham,et al.  Joint Approximate Diagonalization of Positive Definite Hermitian Matrices , 2000, SIAM J. Matrix Anal. Appl..

[10]  Dinh-Tuan Pham,et al.  Blind separation of instantaneous mixtures of nonstationary sources , 2001, IEEE Trans. Signal Process..

[11]  Amrane Houacine,et al.  Neuronal principal component analysis for an optimal representation of multispectral images , 2001, Intell. Data Anal..

[12]  Amrane Houacine,et al.  A new PCA-based method for data compression and enhancement of multi-frequency polarimetric SAR imagery , 2002, Intell. Data Anal..

[13]  Heinz Mathis,et al.  Joint diagonalization of correlation matrices by using gradient methods with application to blind signal separation , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[14]  M. Joho,et al.  Joint diagonalization of correlation matrices by using Newton methods with application to blind signal separation , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[15]  Amrane Houacine,et al.  Statistical characterisation and modelling of SAR images , 2002, Signal Process..

[16]  Toshinao Akuzawa,et al.  Nested Newton's method for ICA and post factor analysis , 2003, IEEE Trans. Signal Process..

[17]  Salim Chitroub.  PCA-ICA neural network model for POLSAR images analysis , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Alexander M. Bronstein,et al.  Relative optimization for blind deconvolution , 2005, IEEE Transactions on Signal Processing.

[19]  Seungjin Choi,et al.  Relative Gradient Learning for Independent Subspace Analysis , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.