RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images

Abstract

The Earth’s observation programs, through the acquisition of remotely sensed hyperspectral images, aim at detecting and monitoring any relevant surface change due to natural or anthropogenic causes. The proposed algorithm, given as input a pair of hyperspectral images, produces as output a binary image denoting in white the changed pixels and in black the unchanged ones. The presented procedure relies on the computation of specific dissimilarity measures and applies successive binarization techniques, which prove to be robust, with respect to the different scenarios produced by the chosen measure, and fully automatic. The numerical tests show superior behavior when other common binarization techniques are used, and very competitive results are achieved when other methodologies are applied on the same benchmarks.