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Polarimetric SAR image classification based on polarimetric decompostition and neural networks theory

In this paper an classification method based on polarimetric decomposition technique and neural network theory, is proposed for polarimetric SAR data sets. The main advantage of this polarimetric decomposition technique is to provide dominant polarimetric scattering properties identification information where the most important kinds of scattering medium can be discriminated. Feature vector extracted from full POLSAR data sets by polarimetric decomposition is used as input data of the feed-forward neural network (FNN). Neural networks have the advantage to be independent to the input signal statistics and the ability to combine many parameters in their inputs. To speed convergence and improve stability of the FNN Kalman filter plus scaled conjugate gradient algorithm is used in the training stage. The NASA/JPL AIRSAR c-band data of San Francisco is used to illustrate the effectiveness of the proposed approach to classification. Quantitative results of performance are provided, as compared to the Wishart classifier.