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

Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Leonardo Santurri

doi:10.1117/12.454159

28 January 2002 Near-lossless compression of multi/hyperspectral image data through a fuzzy-matching-pursuit interband prediction

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Leonardo Santurri

Author Affiliations +

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454159
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

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.

Citation Download Citation

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Leonardo Santurri "Near-lossless compression of multi/hyperspectral image data through a fuzzy-matching-pursuit interband prediction", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454159

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