Symbolic approach to SAR image classification

Abstract

Presents a symbolic approach to synthetic aperture radar (SAR) image classification. The symbolic classifier presented has as input data samples of predefined groups (the learning set) represented in a usual table of values. The learning step organises a complete and discriminant description of each group, using either symbolic objects or disjunctions of symbolic objects. These disjunctions are obtained using symbolic operators and a mutual neighbourhood graph. The classification rule is based on dissimilarity functions between a standard description of an individual and a symbolic object which describes a group. To show the usefulness of this approach in SAR image analysis, images in amplitude format and three and eight looks, showing areas with different degrees of roughness are simulated assuming the multiplicative model. Segmentation of these images, after using the sigma Lee filter to diminish speckle, was performed with a standard region growing algorithm and these segments were used as the input of the symbolic classifier. A Monte Carlo experience is devised, and the feasibility of the symbolic approach is quantitatively assessed.