Pixel-Based Classification of SAR Images Using Feature Attribute Profiles

Abstract

Attribute profiles (APs) are among the most prominent spectral-spatial pixel description tools, widely employed with optical, and hyperspectral images in particular. Their use, however, with the SAR data has been very limited. In this letter, we investigate the use of the recently developed feature attribute profiles (FPs) for the pixel-based classification of SAR images. Compared to low-level features such as intensity and amplitude, FPs are shown to provide high-level features incorporating successfully the spatial information of the pixels. Based on the classification results obtained on real TerraSAR-X images, it is shown that the FPs are capable of more accurately classifying the pixels compared to conventional and widely used SAR feature extraction methods such as gray-level co-occurrence matrices, histograms of oriented gradients, local binary patterns, and Gabor filters.