Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 53, Issue 11, Pages 6286-6292Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2015.2438079
Keywords
Data dimensionality; hyperspectral imagery; low-rank representation (LRR); low-rank subspace representation (LRSR); rank estimation; signal subspace estimation
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In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature.
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