Hyperspectral Unmixing Using Robust Deep Nonnegative Matrix Factorization
Risheng Huang, Huiyun Jiao, Xiaorun Li, Shuhan Chen, Chaoqun Xia
Abstract
Nonnegative matrix factorization (NMF) and its numerous variants have been extensively studied and used in hyperspectral unmixing (HU). With the aid of the designed deep structure, deep NMF-based methods demonstrate advantages in exploring the hierarchical features of complex data. However, a noise corruption problem commonly exists in hyperspectral data and severely degrades the unmixing performance of deep NMF-based methods when applied to HU. In this study, we propose an ℓ2,1 norm-based robust deep nonnegative matrix factorization (ℓ2,1-RDNMF) for HU, which incorporates an ℓ2,1 norm into the two stages of the deep structure to achieve robustness. The multiplicative updating rules of ℓ2,1-RDNMF are efficiently learned and provided. The efficiency of the presented method is verified in experiments using both synthetic and genuine data.