An a-contrario approach for unsupervised change detection in radar images

This paper presents a new approach for unsupervised change detection in pairs of Synthetic Aperture Radar (SAR) images. As changes to detect can have various sizes and intensities which are a priori unknown in most applications, we propose a multiscale approach without considering any a priori information. Using multiscale series of a cumulant-based Kullback-Leibler divergence (CKLD) measure computed between two dates, changes are characterized as areas where the CKLD values vary a lot when the scale varies. In a probabilistic a-contrario framework, a measure of meaningfulness of such an evolution through scale is derived, leading to a criterion free of parameter. Results are presented using a pair of SAR images acquired before and after the volcanic eruption of the Nyiragongo in January 2002 (Congo), showing the robustness of the method with respect to the number of false alarms.