  Several approaches have been proposed to solve the spectral unmixing problem
in hyperspectral image analysis. Among them the use of sparse regression
techniques aims to characterize the abundances in pixels based on a large
library of spectral signatures known a priori. Recently, the integration of
image spatial-contextual information significantly enhanced the performance of
sparse unmixing. In this work, we propose a computationally efficient
multiscale representation method for hyperspectral data adapted to the unmixing
problem. The proposed method is based on a hierarchical extension of the SLIC
oversegmentation algorithm constructed using a robust homogeneity testing. The
image is subdivided into a set of spectrally homogeneous regions formed by
pixels with similar characteristics (superpixels). This representation is then
used to provide prior spatial regularity information for the abundances of
materials present in the scene, improving the conditioning of the unmixing
problem. Simulation results illustrate that the method is capable of estimating
abundances with high quality and low computational cost, especially in noisy
scenarios.
