Spectral-Spatial Joint Noise Estimation for Hyperspectral Images

Hyperspectral images (HSIs) are always corrupted by noise, which will strongly affect the applications. Various denoising algorithms have been proposed for HSIs. Most existing denoising methods have parameters related to intensity of noise. So noise estimation is an essential step in HSI denoising. In our previous work, we have proposed a homogeneous region based noise estimation algorithm. However, we find it often fails on severely corrupted bands. To solve the problem, two improvements are made in this work: 1) depending on strong correlations between bands, a regression based signal-noise separation is adopted; 2) utilizing the identical spatial structure of different bands, a unified homogeneous region segmentation is performed across all bands via clustering of spectral vectors. Then, noise estimation is done using curve fitting according to the segmented homogeneous regions with separated signal and noise components. By this spectral-spatial joint approach, we have significantly improved the accuracy of noise estimation.