Incorporating spatial information into hyperspectral unmixing procedures has
been shown to have positive effects, due to the inherent spatial-spectral
duality in hyperspectral scenes. Current research works that consider spatial
information are mainly focused on the linear mixing model. In this paper, we
investigate a variational approach to incorporating spatial correlation into a
nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing
kernel Hilbert spaces, associated with an ℓ1 local variation norm as the
spatial regularizer, is derived. Experimental results, with both synthetic and
real data, illustrate the effectiveness of the proposed scheme.Comment: 5 pages, 1 figure, submitted to ICASSP 201