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

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