A random patches based edge preserving network for land cover classification using Polarimetric Synthetic Aperture Radar images

Abstract

ABSTRACT A random patches-based edge-preserving network (RPEP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification in this paper. An initial spatial feature extraction is firstly done using the transform domain recursive filtering. From the filtered images, several random patches are chosen and used as convolutional kernels. The designed random patches-based network uses these fix kernels without doing any training process. The multi-scale features extracted by both shallow and deep layers are given to a support vector machine to get an initial classification map. The binary probability maps obtained from the initial classification map are then smoothed using the guided filter as an edge preserving filter. The final classification map is achieved by applying the maximum decision rule. The proposed RPEP method with extraction of robust, consistent, invariant and multi-scale polarimetric-spatial features and also by doing noise reduction with edge preserving filters provides superior classification results compared to several state-of-the-art methods especially in small sample size situations. In addition, RPEP has a simple and fast implementation that makes it a powerful classifier for real applications.