Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 12, Issue 11, Pages 2311-2315Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2474744
Keywords
Abundance estimation; hyperspectral image; similarity-weighting; sparse unmixing; l(p)-regularization
Categories
Funding
- 973 Program [2011CB707104]
- National Science Foundation of China [61372147, 61501188]
- Science Foundation of Shanghai [15ZR1410200]
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In this letter, we propose a novel sparse unmixing model combined with two effective regularization terms: one is a similarity-weighting constraint, and the other is the l(p) (0 < p < 1) norm sparse regularization. The former utilizes the spatial structural correlation, which is presented in the hyperspectral data, to guide the abundance estimation. When compared with the existing graph Laplacian regularization, our similarity-weighting constraint avoids large matrix inversion, and thus, it can be efficiently solved. As for the l(p)-norm, it has numerical advantages over the convex l(1)-norm and better approximates the l(0)-norm theoretically. Moreover, the l(p)-norm regularizer can simultaneously promote sparsity and enforce the abundance sum-to-one constraint. Therefore, this term yields more desirable results in practice. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed model.
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