期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 52, 期 1, 页码 341-354出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2013.2240001
关键词
Collaborative sparse regression; hyperspectral imaging; sparse unmixing; spectral libraries
类别
资金
- European Community's Marie Curie Research Training Networks Programme under Hyperspectral Imaging Network (HYPER-I-NET) [MRTN-CT-2006-035927]
- Portuguese Science and Technology Foundation [PEst-OE/EEI/LA0008/2011]
- Spanish Ministry of Science and Innovation (CEOS-SPAIN project) [AYA2011-29334-C02-02]
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.
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