A New Method for Land Cover Characterization and Classification of Polarimetric SAR Data Using Polarimetric Signatures

Conventional methods for analyzing polarimetric synthetic aperture RADAR (PolSAR) data such as scattering matrix show polarimetric information just in a restricted number of polarization bases, whereas backscattering of the targets has information on wide range of polarizations. In order to solve this problem, polarimetric signatures have been investigated to have a better illustration of the target responses. Polarimetric signatures depict more details of physical information from target backscattering in various polarization bases. This paper presents a new method for generating polarimetric signatures for different features in PolSAR data by changing the polarization basis in the covariance matrix. Furthermore, various land cover classes were evaluated using their polarimetric signatures and the pattern recognition matching methods. On the basis of this background, an object-oriented and knowledge-based classification algorithm is proposed. The main idea of this method is to apply polarimetric signatures of various PolSAR features in the land cover classification. A Radarsat-2 image, acquired in leaf-off season of the forest areas, was chosen for this study. The backscattering from different classes, including six land cover classes: 1) red oak (Or); 2) white pine (Pw); 3) black spruce (Sb); 4) urban (Ur); 5) water (Wa); and 6) ground vegetation (GV) was analyzed by the proposed method. The results reported that the polarimetric signatures of PolSAR features introduce new concepts for the various targets which are different from the polarimetric power signatures. Also, the proposed classification was compared with the object-based form of the supervised Wishart classification as the baseline method. The mean accuracy of the proposed method is 6% better than the supervised Wishart classification.