MATLAB PROJECT
Hyperspectral Image
Unmixing With Endmember Bundles and Group Sparsity Inducing Mixed Norms
Abstract:
Hyperspectral images
provide much more information than conventional imaging techniques, allowing a
precise identification of the materials in the observed scene, but because of
the limited spatial resolution, the observations are usually mixtures of the
contributions of several materials. The spectral unmixing problem aims at
recovering the spectra of the pure materials of the scene (endmembers), along
with their proportions (abundances) in each pixel. In order to deal with the
intra-class variability of the materials and the induced spectral variability
of the endmembers, several spectra per material, constituting endmember
bundles, can be considered. However, the usual abundance estimation techniques
do not take advantage of the particular structure of these bundles, organized
into groups of spectra. In this paper, we propose to use group sparsity by
introducing mixed norms in the abundance estimation optimization problem. In
particular, we propose a new penalty, which simultaneously enforces group and
within-group sparsity, to the cost of being nonconvex. All the proposed
penalties are compatible with the abundance sum-to-one constraint, which is not
the case with traditional sparse regression. We show on simulated and real
datasets that well-chosen penalties can significantly improve the unmixing
performance compared to classical sparse regression techniques or to the naive
bundle approach.
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