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A learning algorithm for neural network is proposed in this paper for classification of fully polarimetric SAR imagery data. Based on a polynomial basis function expansion, the multilayer precepton network was modified such that at output layer the functional form is linearized while the hidden layers remain to be nonlinear. The weighting function in each layer are cascading to form a long vector though which the outputs and inputs are related. This modification allows us to apply the dynamic Kalman filtering technique to adjust the network weighting in a sense of recursive minimum least square error. The new network has features such as very fast learning and built-in optimization of weighting function. The fast learning rate stems from that the weighting updating is not in a fashion of back-propagation which usually takes lengthy time to finish the learning process. The efficiency of the proposed network to classification polarimetric SAR image was illustrated. For purpose of comparison, the commonly used backpropagation (BP) network and a recently developed fast-learning (FL) were also tested. In particular, the optimal polarizations for best discriminating the terrain covers from polarimetric was implemented through the Kalman filtered neural network, where the only necessary inputs are linear- polarized channels data (hh, vv, vh or hv). Excellent performance of the proposed algorithm is obtained in terms of learning speed and classification accuracy.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. |
