Structural optimization for accurate characterization of urban areas in hyperspectral datasets

Accurately estimating the urbanization process is a key-factor for the actual implementation of the sustainable development goals identified by transnational institutions and agencies. In order to retrieve precise characterization of the anthropogenic extents and a sound human-environment interaction assessment, the analysis of Earth observations (EOs) plays a crucial role. Especially, the use of nonlinear spectral investigation can improve the description of geometrically and morphologically complex scenes, so that anthropogenic settlements and dynamics can be properly outlined. In this paper, we propose a novel method for directly assessing the distribution of materials and elements in hyperspectral images by means of a structural optimization approach. Experimental results show how the proposed approach is able to deliver accurate and reliable characterization of urban materials and extents.