DMCA
Collaborative Sparse Regression For Hyperspectral Unmixing (2013)
| Venue: | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
| Citations: | 11 - 4 self |
BibTeX
@MISC{Iordache13collaborativesparse,
author = {Marian-daniel Iordache and José M. Bioucas-dias and Antonio Plaza},
title = {Collaborative Sparse Regression For Hyperspectral Unmixing},
year = {2013}
}
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Abstract
Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach.
Keyphrases
collaborative sparse regression hyperspectral unmixing mixed pixel real image large library field spectroradiometer pure spectral signature hyperspectral imaging new joint sparse regression strategy sparse unmixing low number linear combination sparse regression framework optimal subset real hyperspectral data set pixelwise independent approach observed image signature unmixing result joint sparse regression problem data set spectral library experimental result
