Nonlinear filtering of hyperspectral images with anisotropic diffusion

Abstract

A vectorial extension of the scalar anisotropic diffusion nonlinear filtering process applied on hyperspectral images is presented. In a first step, data are projected in a transformed space with a Maximum Noise Fraction transform, allowing the new components to be sorted in order of signal to noise ratio. The filtering is adapted to the signal to noise ratio of each component and a spectral dissimilarity vectorial measure is used in the filtering process. The inverse transform allows the filtered data to be reprojected in the original space. This process is useful for denoising hyperspectral images and for reducing spatial and spectral variability in each class of interest, leading to increase the performance of further segmentation or classification algorithms.