Abstract
Change detection is the process of identifying differences in the state of an object or scene after the occurence of an event. In this paper, we will present several similarity measures for automatic synthetic aperture radar (SAR) change detection, which can be classified into two families: the first regroups the measures based on pixel radiometry and the second collects the detectors based on local statistics. This article details a judicious method for SAR image change detection using Dezert–Smarandache Theory (DSmT). On the one hand, a Rayleigh distribution function is used to characterize globally the radar texture data, which allows mass assignment through the Kullback–Leibler distance. On the other hand, local pixel measurements are introduced through the Rayleigh Distribution Ratio indicator to refine the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. This technique has been applied on both simulated and real data and allows very satisfactory change detection results.
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