Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images

This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectraloral variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to...