LAUSR

Band selection (BS), which can reduce the spectral dimensionality effectively, has become one of the most popular topics in hyperspectral image (HSI) analysis. Recently, sparse representation-based BS has emerged as a popular tool. The existing sparse models mainly focus on minimizing reconstruction error and sparsity, while do not fully exploit the unique correlations among hundreds of continuous bands, which may cause representative bands missed and highly correlated bands selected. Therefore, this article proposes the spectral correlation-based diverse BS (SCDBS) for HSIs to improve representativeness and diversity of the selected bands. Specifically, a correlation derived weight is used to perform weighted sparse reconstruction to select the bands that are more correlated with the whole HSI, and a correlation minimization term is designed to remove the highly correlated bands simultaneously. In addition, the proposed method imposes an adjustable sparse constraint by using an $\ell _{2,0< p\leq 1}$ norm, which extends and unifies the commonly used $\ell _{2,1}$ norm to provide more flexible sparsity level. To optimize the proposed BS model, an iteration algorithm with relatively low computational cost is designed, of which the convergence is theoretically presented. Experimental results on three benchmark datasets have demonstrated that the proposed SCDBS outperforms state-of-the-art methods in HSI classification.