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

Abstract

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs 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 better extracting intrinsic and deep features of HSIs. Based on a fully cascaded multi-scale convolutional network, SSGN can simultaneously deal with the 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 performs better at mixed noise removal than the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

arXiv commentAccept by IEEE TGRS

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Cited by in corpus (6)

• Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
• Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks
• Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
• Nonlocal Patch-Based Fully-Connected Tensor Network Decomposition for Remote Sensing Image Inpainting
• Iterative Low-rank Network for Hyperspectral Image Denoising
• Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification