Hyperspectral unmixing with material variability using social sparsity

Travis Meyer 1 Lucas Drumetz 2 Jocelyn Chanussot 2 Andrea Bertozzi 1 Christian Jutten 3
2 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
3 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
Abstract : We apply social-norms for the first time to the problem of hyperspectral unmixing while modeling spectral variability. These norms are built with inter-group penalties which are combined in a global intra-group penalization that can enforce selection of entire endmember bundles; this results in the selection of a few representative materials even in the presence of large endmembers bundles capturing each material's variability. We demonstrate improvements quantitatively on synthetic data and qualitatively on real data for three cases of social norms: group, elitist, and a fractional social norm, respectively. We find that the greatest improvements arise from using either the group or fractional flavor.
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Travis Meyer, Lucas Drumetz, Jocelyn Chanussot, Andrea Bertozzi, Christian Jutten. Hyperspectral unmixing with material variability using social sparsity. 23th IEEE International Conference on Image Processing (ICIP 2016) , IEEE, Sep 2016, Phoenix, United States. ⟨10.1109/ICIP.2016.7532746⟩. ⟨hal-01364247⟩

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