Nonnegative matrix factorization with region sparsity learning for hyperspectral unmixing

作者：Qian, Bin; Tong, Lei^*; Tang, Zhenmin; Shen, Xiaobo

来源：International Journal of Wavelets, Multiresolution and Information Processing, 2017, 15(6): 1750063.

DOI：10.1142/S0219691317500631

• Hyperspectral unmixing
• nonnegative matrix factorization
• sparsity learning
• spectral-spatial information
• graph cut

Hyperspectral unmixing is one of the most important techniques in the remote sensing image analysis tasks. In recent decades, nonnegative matrix factorization (NMF) has been shown to be effective for hyperspectral unmixing due to the strong discovery of the latent structure. Most NMFs put emphasize on the spectral information, but ignore the spatial information, which is very crucial for analyzing hyperspectral data. In this paper, we propose an improved NMF method, namely NMF with region sparsity learning (RSLNMF), to simultaneously consider both spectral and spatial information. RSLNMF defines a new sparsity learning model based on a small homogeneous region that is obtained via the graph cut algorithm. Thus RSLNMF is able to explore the relationship of spatial neighbor pixels within each region. An efficient optimization scheme is developed for the proposed RSLNMF, and its convergence is theoretically guaranteed. Experiments on both synthetic and real hyperspectral data validate the superiority of the proposed method over several state-of-the-art unmixing approaches.

• 出版日期2017-11
• 单位南京理工大学; 北京工业大学