Nonlinear PCA based Polarimetric Decomposition

The latest years demonstrated the operational level reached by polarimetric data processing techniques. The next generation of spaceborne Synthetic Aperture Radar satellites will implement full- or dual- polarimetric capabilities. In few years a huge amount of data will have to be processed in a fast and reliable way, implementing polarimetric decompositions or accurate classifications. Two neural network approaches for fast and accurate processing of polarimetric data are presented. In the first approach a neural network based processing chain for fast model based polarimetric decomposition is developed, while in the second approach a Non-Linear Principal Component Analisys of polarimetric data has been performed using an Auto-Associative Neural Network. The results show a considerable reduction of computational effort and a substantial data compression with a minimun loss of information.