BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images

Abstract

We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR.