Journal
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
Volume 9, Issue 6, Pages 1142-1155Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2015.2419184
Keywords
Linear mixture model; spectral unmixing; endmember extraction; nonnegative matrix factorization (NMF); optimization; greedy pursuit
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Funding
- National Science Foundation [CCF-1117545]
- Army Research Office [60219-MA]
- Office of Naval Research [N000141210765]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1422995] Funding Source: National Science Foundation
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The separability assumption turns the nonnegative matrix factorization (NMF) problem tractable, which coincides with the pure pixel assumption and provides new insights for the hyperspectral unmixing problem. Based on this assumption, and starting from the data self-expressiveness perspective, we formulate the unmixing problem as a joint sparse recovery problem by using the data itself as a dictionary. Moreover, we present a quasi-greedy algorithm for this problem by employing a back-tracking strategy. In comparison with the previous greedy methods, the proposed method can refresh the candidate pixels by solving a small fixed-scale convex sub-problem in every iteration. Therefore, our method has two important characteristics: (i) enhanced robustness against noise; (ii) moderate computational complexity and scalability to large dataset. Finally, computer simulations on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.
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