A Novel NMF Guided for Hyperspectral Unmixing From Incomplete and Noisy Data

The nonnegative matrix factorization (NMF)-combined spatial–spectral information has been widely applied in the unmixing of hyperspectral images (HSIs). However, how to select the appropriate similarity pixels and explore the spatial information and how to adapt the unmixing algorithm to complex data are both great challenges. In this article, we propose a novel unmixing method named spatial–spectral neighborhood preserving NMF (SSNPNMF) for incomplete and noisy HSI data. First, a spatial–spectral kernel regularizer is introduced to preprocess the HSI, which can reduce noise and complete missing elements. Second, a distance metric SSD based on spatial–spectral information is designed to select similar pixels in the image. Subsequently, the spatial–spectral relationship of the selected first k similar pixels is used to reconstruct the image and obtain the reconstruction matrix. Finally, the reconstruction matrix is used to constrain the abundances and improve the unmixing performance. Experimental results on synthetic data and Cuprite data indicate that SSNPNMF has a more effective unmixing performance compared with the state-of-the-art methods.