Robust techniques for coherent change detection using COSMO-SkyMed SAR images

The satellite-borne SAR (Synthetic Aperture Radar) is a quite promising tool for high-resolution geo-surface measurement. Recently, there has been a great interest in Coherent Change Detection (CCD), where the coherence between two SAR images is evaluated and analyzed to detect surface changes. The sample coherence threshold may be used to distinguish between the changed and unchanged regions in the scene. Using COSMO-SkyMed (CSK) images, we show that for changed areas, the coherence is low but not completely lost. This situation, which is caused by the presence of bias in the coherence estimate, considerably degrades the performance of the sample threshold method. To overcome this problem, robust detection in inhomogeneous data must be considered. In this work, we propose the application and improvement of three techniques: Mean Level Detector (MLD), Ordered Statistic (OS) and Censored Mean Level Detector (CMLD), all applied to coherence in order to detect surface changes. The probabilities of detection and false alarm are estimated experimentally using high-resolution CSK images. We show that the proposed method, CMLD with incorporation of guard cells (GC) in the range direction, is robust and allows for nearly 4% higher detection probability in case of low false alarm probability.