Adaptive polarimetric change detection and interpretation based onsupervised ground-cover classification using SAR and optical imagery

In this paper, we propose and illustrate a methodology for classifying the change detection results generated from repeatpass polarimetric RADARSAT-2 images and segmenting only the changes of interest to a given user while suppressing all other changes. The detected changes are first classified based on generated supervised ground-cover classification of the polarimetric SAR images between which changes were detected. In the absence of reliable ground truth needed for generating supervised classification training sets, we rely on the use of periodically acquired high-resolution, multispectral optical imagery in order to classify the manually selected training sets before computing their classes' statistics from the SAR images. The classified detected changes can then be segmented to isolate the changes of interest, as specified by the user and suppress all other changes. The proposed polarimetric change detection, classification and segmentation method overcomes some of the challenges encountered when visualizing and interpreting typical raw change results. Often these non-classified change detection results tend to be too crowded, as they show all the changes including those of interest to the user as well as other non-relevant changes. Also, some of the changes are difficult to interpret, especially those which are attributed to a mixture of the backscatters. We shall illustrate how to generate, classify and segment polarimetric change detection results from two SAR images over a selected region of interest.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.