Near-lossless compression of multi/hyperspectral image data through a fuzzy matching-pursuit interband prediction

In this work, near-lossless compression yielding strictly bounded reconstruction error, is proposed for high-quality compression of remote sensing images. A space-varying linear-regression prediction is obtained through fuzzy-logic techniques as a problem of matching pursuit, in which a predictor different for every pixel is obtained as an expansion in series of a finite number of prototype nonorthogonal predictors, that are calculated in a fuzzy fashion as well. To enhance entropy coding, the spatial prediction is followed by context-based statistical modeling of prediction errors. Performance comparisons with JPEG 2000 and previous works by the authors, highlight the advantages of the proposed fuzzy approach to data compression.