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

TL;DR

This paper introduces a spatial-spectral gradient network (SSGN) that effectively removes mixed noise from hyperspectral images by leveraging spectral and spatial features, outperforming existing methods in accuracy and efficiency.

Contribution

The paper proposes a novel fully cascaded multi-scale convolutional network that simultaneously handles various noise types in hyperspectral images using a spatial-spectral gradient learning strategy.

Findings

SSGN outperforms state-of-the-art algorithms in denoising accuracy.

SSGN demonstrates superior visual quality in noise removal.

SSGN reduces processing time compared to existing methods.

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.