CP-IRGS: A Region-Based Segmentation of Multilook Complex Compact Polarimetric SAR Data

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Mohsen Ghanbari, David A. Clausi, Linlin Xu
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2021
• Subjects: Complex Wishart distribution; Markov random fields (MRFs); multilook complex compact polarimetry; region-based; sea-ice; synthetic aperture radar (SAR); Ocean engineering; TC1501-1800; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1109/JSTARS.2021.3089874; https://doaj.org/article/258a833bbb5a4a5db1c6c6cce23d106b

The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.