Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 23, Issue 12, Pages 5510-5518Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2014.2362056
Keywords
Hyperspectral imaging; blind unmixing; ADMM; sparse regularization
Funding
- Agence Nationale pour la Recherche, France [ANR-12-BS03-003]
- Regional Council of Provence-Alpes-Cote d'Azur
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This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem and is solved with the alternating direction method of multipliers. The second one accounts for signal-dependent noise and is addressed with a reweighted least squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.
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