Polarimetric SAR Data Classification Using Neural Networks

Abstract

In this paper, we consider classification methods using neural networks for multi-frequency polarimetric SAR data. This data was obtained by SIR-C inside Space Shuttle (Space Radar Lab I) . We propose a learning algorithm for competitive neural networks and find the most effective feature vector. The proposed algorithm is as follows: First, weight vectors are trained by one of three basic algorithms (LVQ1, LVQ2.1, and OLVQ1) . Next, they are re-trained and tuned up by the LVQ2.1 for a narrower window area around the category boundaries.
In these experiments, we demonstrate the re-training effects. Then nine types of feature vectors are adopted to the classification methods, which in turn are compared to the conventional methods such as the maximum likelihood method and the layered neural networks (BP) with respect to average accuracy, classification image quality, and computational time. As a result, the proposed method, especially, OLVQ1 + LVQ2.1 outperforms other methods and the feature vector which is composed of back-scattering power of cross polarization for L and C bands generates the best classification accuracies and noiseless images.