TERRAIN CLASSIFICATION IN SAR IMAGES USING PRINCIPAL COMPONENTS-ANALYSIS AND NEURAL NETWORKS

The development or a neural network-based classifier for classifying t hree distinct scenes (urban, park and water) from several polarized SA R images of San Francisco Bay area is discussed. The principal compone nt (PC) scheme or Karhunen-Loeve (KL) transform is used to extract the salient features of the input data, and to reduce the dimensionality of the feature space prior to the application to the neural networks. Employing PC scheme along with polarized images used in this study, le d to substantial improvements in the classification rates when compare d with previous studies. When a combined polarization architecture is used the classification rate for water, urban and park areas improved to 100 %, 98.7 %, and 96.1 %, respectively.