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Proceedings Paper

Near-lossless compression of multi/hyperspectral image data through a fuzzy-matching-pursuit interband prediction
Author(s): Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Leonardo Santurri
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Paper 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.

Paper Details

Date Published: 28 January 2002
PDF: 12 pages
Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454159
Show Author Affiliations
Bruno Aiazzi, CNR (Italy)
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Stefano Baronti, CNR (Italy)
Leonardo Santurri, CNR (Italy)

Published in SPIE Proceedings Vol. 4541:
Image and Signal Processing for Remote Sensing VII
Sebastiano Bruno Serpico, Editor(s)

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