Spectral–Spatial-Weighted Multiview Collaborative Sparse Unmixing for Hyperspectral Images

Abstract

Spectral unmixing is an important task in hyperspectral image (HSI) analysis and processing. Sparse representation has become a promising semisupervised method for remotely sensed hyperspectral unmixing and incorporating the spectral or spatial information to improve the spectral unmixing results under a weighted sparse unmixing framework is a recent trend. While most methods focus on analyzing HSI by exploring the spatial information, it is known that hyperspectral data are characterized by its large contiguous set of wavelengths. This information can be naturally used to improve the representation of pixels in HSI. In order to take the advantage of the hyper spectral information as well as the spatial information for hyperspectral unmixing, in this article, we explore and introduce a multiview data processing approach through spectral partitioning to benefit from the abundant spectral information in HSI. Some important findings on the application of multiview data set in sparse unmixing are discussed. Meanwhile, we develop a new spectral–spatial-weighted multiview collaborative sparse unmixing (MCSU) model to tackle such a multiview data set. The MCSU uses a weighted sparse regularizer, which includes both multiview spectral and spatial weighting factors to further impose sparsity on the fractional abundances. The weights are adaptively updated associated with the abundances, and the proposed MCSU can be solved by the alternating direction method of multipliers efficiently. The experimental results on both the simulated and real hyperspectral data sets demonstrate the effectiveness of the proposed MCSU, which can significantly improve the abundance estimation results.