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This letter addresses the joint sparse recovery problem, which is a hot topic in the compressed sensing (CS) theory and its various applications. Inspired by particle swarm optimization (PSO) algorithm and some sparse recovery algorithms, a novel swarm intelligence algorithm called M-SISR is proposed to solve the problem. In M-SISR, the initial positions of the swarm are designed using the -thresholding algorithm, and the update strategy is designed using the ideas of PSO and some sparse recovery algorithms. Theoretical analysis shows the good property of the update strategy, and numerical simulations on random Gaussian data illustrate the efficiency of M-SISR.

Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with the local minimizers of NMF. We present two novel initialization strategies that is based on CUR decomposition, which is physically meaningful. In the experimental test, NMF with the new initialization method is used to unmix the urban scene which was captured by airborne visible/infrared imaging spectrometer (AVIRIS) in 1997, numerical results show that the initialization methods work well.

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