Sparse Unmixing of Hyperspectral Data by Exploiting Joint-Sparsity and Rank-Deficiency
In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined l2,p-norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy.
Hyperspectral unmixing, joint-sparse, low-rank representation, abundance estimation.
Cognitive SATP for Airborne Radar Based on Slow-Time Coding
Space-time adaptive processing (STAP) techniques have
been motivated as a key enabling technology for advanced airborne
radar applications. In this paper, the notion of cognitive radar is
extended to STAP technique, and cognitive STAP is discussed.
The principle for improving signal-to-clutter ratio (SCNR) based
on slow-time coding is given, and the corresponding optimization
algorithm based on cyclic and power-like algorithms is presented.
Numerical examples show the effectiveness of the proposed method.
Space-time adaptive processing (STAP),
signal-to-clutter ratio, slow-time coding.