Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models. In essence, this approach exploits the fact that HSI pixels can be associated with a small number of constituent pure materials. However, unlike traditional least-squares-based methods, sparsity-based techniques do not require a preselection of endmembers and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. In addition, this perspective has been extended so as to exploit the spatial homogeneity of abundance vectors. As a result, these techniques have been reported to provide improved estimation accuracy. In this letter, we present an alternative approach that is able to relax, yet exploit, the assumption of spatial homogeneity by introducing a model that captures both similarities and differences between neighboring abundances. In order to valiyear this approach, we analyze our model using simulated as well as real hyperspectral data acquired by the {HyMap} sensor.     «

Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models. In essence, this approach exploits the fact that HSI pixels can be associated with a small number of constituent pure materials. However, unlike traditional least-squares-based methods, sparsity-based techniques do not require a preselection of endmembers and are thus able to simultaneously estimate the underlying active materials along with their respective abundances...     »