Region-Based Multiview Sparse Hyperspectral Unmixing Incorporating Spectral Library

Abstract

Hyperspectral image (HSI) is characterized by its huge contiguous set of wavelengths. It is possible and needed to benefit from the “hyper” spectral information as well as the spatial information. For this purpose, we propose a new multiview data generation approach that takes full advantage of the rich spectral and spatial information in HSI, by dividing the original HSI into several spatially homogeneous regions with different band margins. Then, a new sparse unmixing algorithm, called region-based multiview sparse unmixing (RMSU), is presented to tackle such a multiview data model in this letter. The RMSU algorithm combines the multiview learning and a priori information to improve the performance of sparse unmixing by incorporating the multiview information and spectral library into the dictionary learning framework. We also show that RMSU can serve as a dictionary pruning algorithm, which provides a possibility that unmixing algorithms could have higher accuracy and efficiency. Experimental results on both simulated and real hyperspectral data demonstrate the effectiveness of the proposed RMSU algorithm both visually and quantitatively.