A novel feature extraction method for the classification of SAR images

This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood for each pixel. LPs correspond to a certain number of layers of local homogenous connected components. Using LPs, a feature vector (local primitive pattern, LPP) is constructed for each pixel. The feature vector includes information about the sizes and contrast differences of LPs within a disk as well as the repetitive frequency of LPs outside that disk. To test the efficiency of LPP, support vector machine (SVM) classification is utilized.