A graph-based method for non-linear unmixing of hyperspectral imagery

In this paper, we present an unmixing algorithm that is capable to determine endmembers and their abundances in hyperspectral imagery under non-linear mixing assumptions. The algorithm is an based upon the popular N-findR method, but uses distances between points in spectral space instead of the spectral values. These distances are defined as shortest-path distances in a nearest-neighbor graph, hereby respecting the non-trivial geometry of the data manifold in the case of nonlinearly mixed pixels. This allows the algorithm to be applied under non-linear mixing conditions. A demonstration on artificial data is given.