Statistical method based on simultaneous diagonalisation for POLSAR images analysis

In [1], we have proposed a PCA-ICA neural network model for POLSAR image analysis. We propose here a new method that is full based on an algebraic statistical formulation and that is well justified from the mathematical point of view. Its advantage is that it is easy in its implementation that requires certain subroutines of the inverse matrix and the eigenvalues/eigenvectors decomposition. While the PCA-ICA neural network model is very sensible to both the probabilistic model of the data [2], [3] and the power of the noise that corrupts the input data [1]. In addition, it requires more computation times in its learning process. Thus, the goal of this paper is to arise the power of each method and by this way we try to open new issues in the concern of working out new methods that accumulate the advantages of each method while avoiding their disadvantages.