SAR IMAGE SEGMENTATION USING TEXTURAL INFORMATION AND NEURAL CLASSIFIERS

This paper describes the application of neural network techniques to t he automatic segmentation of Synthetic Aperture Radar (SAR) images. Tw o techniques have been studied : the Multilayer Perceptron (MLP) and t he Learning Vector Quantization (LVQ). For an efficient classification , the neural network is applied to a set of features extracted from th e SAR image. Examples of suitable features are the radiometric informa tion, the spatial frequency and so on. Two techniques are studied for feature extraction : the Gabor filter and the Principal Component Anal ysis (PCA). The paper reports the study in terms of correct classifica tion rates related to four classification schemes obtained by a suitab le combination of the feature extraction stage and neural network stag e.