Share Email Print
cover

Proceedings Paper

Near-lossless compression of hyperspectral data through classified spectral prediction
Author(s): Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Andrea Garzelli; Cinzia Lastri; Leonardo Santurri
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents a novel scheme for lossless/near-lossless hyperspectral image compression, that exploits a classified spectral prediction. MMSE spectral predictors are calculated for small spatial blocks of each band and are classified (clustered) to yield a user-defined number of prototype predictors for each wavelength, capable of matching the spatial features of different classes of pixel spectra. Unlike most of the literature, the proposed method employs a purely spectral prediction, that is suitable for compressing the data in band-interleaved-by-line (BIL) format, as they are available at the output of the on-board spectrometer. In that case, the training phase, i.e., clustering of predictors for each wavelength, may be moved off-line. Thus, prediction will be slightly less fitting, but the overhead of predictors calculated on-line is saved. Although prediction is purely spectral, hence 1D, spatial correlation is removed by the training phase of predictors, aimed at finding statistically homogeneous spatial classes matching the set of prototype spectral predictors. Experimental results on AVIRIS data show improvements over the most advanced methods in the literature, with a computational complexity far lower than that of analogous methods by other authors.

Paper Details

Date Published: 17 September 2005
PDF: 7 pages
Proc. SPIE 5915, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, 59150M (17 September 2005); doi: 10.1117/12.618643
Show Author Affiliations
Bruno Aiazzi, Institute of Applied Physics Nello Carrara, IFAC-CNR (Italy)
Luciano Alparone, Univ. of Florence (Italy)
Stefano Baronti, Institute of Applied Physics Nello Carrara, IFAC-CNR (Italy)
Andrea Garzelli, Univ. of Siena (Italy)
Cinzia Lastri, Institute of Applied Physics Nello Carrara, IFAC-CNR (Italy)
Leonardo Santurri, Institute of Applied Physics Nello Carrara, IFAC-CNR (Italy)


Published in SPIE Proceedings Vol. 5915:
Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications
Mark S. Schmalz, Editor(s)

© SPIE. Terms of Use
Back to Top