Abstract
Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation-based unmixing (CRU) for hyperspectral images is proposed. Different from imposing the sparseness constraint on training samples in sparse representation, collaborative representation emphasizes the collaboration of training samples. Furthermore, its closed form solution greatly improves computational efficiency. In the experiments, synthetic and the real hyperspectral data are used to evaluate the effectiveness and efficiency of the proposed collaborative representation-based hyperspectral unmixing algorithm.
Highlights
Due to the high spectral resolution of hyperspectral remote sensing, it is widely used in mineral exploration, military, vegetation investigation, environmental protection, etc [1]
For a given hyperspectral image (HSI), the hybrid mechanism in each pixel is complex, and the spectral unmixing models can be categorized into two types: (1) Linear spectral mixing model (LSMM); (2) Nonlinear spectral mixing model (NLSMM) [2]
The most common abundance estimation algorithms are unconstrained least squares (UCLS), sum-to-one constrained least squares (SCLS), non-negativity constrained least squares (NCLS), The associate editor coordinating the review of this manuscript and approving it for publication was Qiangqiang Yuan
Summary
Due to the high spectral resolution of hyperspectral remote sensing, it is widely used in mineral exploration, military, vegetation investigation, environmental protection, etc [1]. SUnSAL-TV introduces a TV term based on SUnSAL, and it considers the spatial neighborhoods information to improve the accuracy of hyperspectral unmixing It lacks the effective constraints on abundance coefficients. The l2- norm minimization is a convex, smooth, and differentiable function, resulting in a closed-form solution Both simulated and real data experiments are conducted to verify the reliability of the proposed collaborative representation-based hyperspectral unmixing algorithm. Since the l1-norm minimization is a convex, non-smooth, global non-differentiable problem, it is difficult to perform singular value decomposition of X To solve this problem, we propose the collaborative representation for HSI unmixing
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