Combined intensity and coherent change detection for synthetic aperture radar

Coherent change detection using paired synthetic aperture radar images is performed using a classical coherence estimator applied under an assumption of complex Gaussian data. The magnitudes of the resulting coherence estimates are plotted as an image and used to gauge changes in the observed scene. In this paper, a two-stage change statistic that combines non-coherent and coherent change detection algorithms is proposed. In the first stage, a non-coherent intensity change detector is applied to test for changes caused by the displacement of a sizable object using the sample variance ratio test. The sample pairs that failed the first stage are used as an input to the second stage. The second stage test uses an alternative coherence estimator that assumes equal population variances, to detect subtle changes such as tire tracks and footprints. We show experimentally that the proposed method not only has a superior change detection performance over the classical coherent change detector, but also over either the non-coherent intensity change detector or the alternative coherent change detector, alone. Experimental results are presented to show the effectiveness and robustness of the proposed algorithm for SAR change detection.