Using contextual information to improve SAR CCD: Bayesian contextual coherent change detection (BC CCD)

Semi-automated, subtle, ground surface change detection using synthetic aperture radar coherent change detection (SAR CCD) suffers from a high false alarm rate. Errors due to dispersion in coherence estimates propagate into change estimates based on coherence values. Low coherence values indicating ground surface changes also map to non-salient world states, such as leaf movement and radar shadows. In this paper, we address these issues by incorporating contextual information from a multispectral land-cover classification into SAR CCD using Bayesian approach, called Bayesian Contextual Coherent Change Detection (BC CCD). We demonstrate improved change detection performance of BC CCD with data over diverse areas using ROC curves. BC CCD shows substantial improvements over unprocessed SAR CCD and the SAR CCD false alarm reduction method Clutter Location Estimation and Negation (CLEAN) CCD. Supervised classification on the multispectral (MS) image coupled with a maximum entropy prior stipulation yields the highest performance gains.