ENTROPY-BASED NOISE REDUCTION IN HYPERSPECTRAL IMAGES
The goal of dimensionality reduction of hyperspectral images is to reduce the number of spectral channels, either by selection or by combination, without losing too much of the image information. The paper investigates the use of entropy as a measure of information content, and addresses the associated difficulties in high-dimensional feature spaces. A nearest neighbor solution is studied with an adapted measure for neighborhood volumes. It appears this also provides a straightforward method to reduce image noise in the spectral domain, which also provides spatial noise reduction, while preserving spatial details and image sharpness.