Independent Component Analysis by Using Joint Cumulants and its Application to Remote Sensing Images

In this paper, a joint cumulant independent component analysis (JC-ICA) algorithm is presented. It utilizes the higher order joint cumulants to extract independent components and can be implemented efficiently by a neural network. Its application in SAR (synthetic aperture radar) image analysis is presented and a comparison is also made with two other ICA methods. The results show the usage in image analysis and separation. Because the algorithm is based on statistics of order higher than the second, it is suitable also for applications to data with non-Gaussian distributions in blind signal processing.

[1]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[2]  C. Chen,et al.  A new independent component analysis (ICA) method and its application to SAR images , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[3]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[4]  Andrzej Cichocki,et al.  Modified Herault-Jutten Algorithms for Blind Separation of Sources , 1997, Digit. Signal Process..

[5]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[6]  Xiaohui Zhang,et al.  Independent component analysis for remote sensing study , 1999, Remote Sensing.

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

[8]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[9]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[10]  Erkki Oja,et al.  The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.

[11]  I. Ginsberg,et al.  Unsupervised hyperspectral image analysis using independent component analysis , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[12]  E. Oja,et al.  Independent Component Analysis , 2013 .

[13]  S. Omatu,et al.  Pattern recognition with neural networks , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[14]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[15]  Juha Karhunen,et al.  A Unified Neural Bigradient Algorithm for robust PCA and MCA , 1996, Int. J. Neural Syst..

[16]  J. Karhunen,et al.  A bigradient optimization approach for robust PCA, MCA, and source separation , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  M. Rosenblatt.  Stationary sequences and random fields , 1985 .

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

[19]  Shun-ichi Amari,et al.  Adaptive blind signal processing-neural network approaches , 1998, Proc. IEEE.

[20]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[21]  Andrzej Cichocki,et al.  Neural network approach to blind separation and enhancement of images , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

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

[23]  Harold H. Szu,et al.  ICA neural net to refine remote sensing with multiple labels , 2000, SPIE Defense + Commercial Sensing.

[24]  T. Tu.  Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis approach , 2000 .