Unsupervised Segmentation of Multilook Compact Polarimetric Sar Data based on Complex Wishart Distribution

Mohsen Ghanbari, David A. Clausi, Linlin Xu, Mingzhe Jiang

Abstract: The Canadian RADARSAT Constellation Mission (RCM) proposes a new synthetic aperture radar (SAR) data mode called compact (hybrid or partial) polarimetry (CP) in a wide swath. Compact polarimetry maximizes the measurement potential if the multilook complex (MLC) coherence matrix of the SAR backscattered field is used. The MLC CP coherence matrix follows the Wishart distribution. In this paper, an unsupervised region-based semantic segmentation of the MLC CP coherence matrix data using the complex Wishart distribution is presented. The segmentation method is an extension of the iterative region growing with semantics (IRGS) to complex CP data. The proposed algorithm is called CP-IRGS and is formulated based on conditional random fields (CRFs) incorporating edge strength over the image. Applications of the algorithm are demonstrated using a simulated MLC CP data set and a real single-look complex (SLC) quadrature polarimetric (QP) SAR data set which is used to derive the MLC CP data.