Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSI applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for effectively extracting intrinsic and deep features of HSIs. Based on a fully cascaded multiscale convolutional network, SSGN can simultaneously deal with different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN outperforms at mixed noise removal compared with the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.