Multiscale Spatial–Spectral Invertible Compensation Network for Hyperspectral Remote Sensing Image Denoising

Abstract

Hyperspectral image (HSI) has fine spectral resolution and abundant spatial information to detect subtle differences between targets. However, it is heavily contaminated with noise due to sensor design and atmospheric radiative transfer, resulting in spectral shifts and spatial discontinuities. Current denoising methods usually establish constraints directly on the ground truth and denoised image, lacking supervision of intermediate parameters of the network, resulting in insufficient model constraints and poor convergence. In addition, existing methods do not consider spatial-spectral compensation, so the denoising results have obvious spatial-spectral distortion. To this end, we propose a novel multiscale spatial-spectral invertible compensation network (MSIC-Net) for HSI denoising. The method constructs an invertible spatial-spectral compensation (ISSC) module, which supervises intermediate features through inverse constraints, realizes the circulation of multiscale information, and improves the stability of the model. At the same time, we also introduce style transfer for spatial-spectral compensation, which uses its superior fine feature control ability to precisely compensate for the lost spatial and spectral detail features. The method is extensively validated experimentally and categorically on simulated and real datasets. The experimental results show that MSIC-Net outperforms other state-of-the-art denoising methods in quantitative and qualitative evaluations.