Unsupervised Classification of Polarimetirc SAR Image Via Improved Manifold Regularized Low-Rank Representation With Multiple Features

Abstract

In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image unsupervised classification method is proposed. It combines three typical features, including polarimetric data features (coherent matrix), polarimetric decomposition features (Krogager, Freeman, Yamaguqi, Neuman, and H/A/α decomposition), and gray-level co-occurrence matrix features to comprehensively describe the data characteristics. And it also proposes a symmetric revised Wishart (SRW) distance-derived manifold regularized low-rank representation (SRWM_LRR) method to deeply exploit the geometry data structure. The low-rank representation (LRR) is used to capture the intrinsic global structure of PolSAR data and the manifold regularization is employed to detect the local structure of the data, in which SRW distance is introduced to measure the similarity between different pixels for describing the local manifold structure. This algorithm considers the specific statistics property in PolSAR data and simultaneously integrates multiple features in perspective of data geometry structure to represent pixels for achieving a better classification performance. The effectiveness and practicability of the proposed method are demonstrated by datasets obtained either in spaceborne or airborne SAR system, including the Flevoland dataset (AIRSAR L-Band) extensively used in land classification cover, and Xi'an dataset (RadarSAT-2 C-Band). Compared with the traditional Wishart classifier, Euclidean and SRW distance-based spectral clustering and LRR, the proposed method shows improvement in accuracy and efficiency as well as a better visualization result.