1. Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing.
- Author
-
Huang, Jie, Huang, Ting-Zhu, Deng, Liang-Jian, and Zhao, Xi-Le
- Subjects
- *
HYPERSPECTRAL imaging systems , *SPARSE approximations , *IMAGE processing , *NUMERICAL analysis , *ALGORITHMS - Abstract
Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size $m\times n$ is partitioned to have one row block and $s$ column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., $s=n$) and the joint (i.e., $s=1$) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called joint-sparse-blocks and low-rank unmixing. In particular, for the joint-sparse-blocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF