Multiband Lossless Compression of Hyperspectral Images

Hyperspectral images exhibit significant spectral correlation, whose exploitation is crucial for compression. In this paper, we investigate the problem of predicting a given band of a hyperspectral image using more than one previous band. We present an information-theoretic analysis based on the con...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2009-04, Vol.47 (4), p.1168-1178
Main Author: Magli, E.
Format: Article
Language:English
Subjects:
3-D prediction
Algorithms
Applied geophysics
Compressing
Compression algorithms
Conditional entropy
Digital imagery
Digital images
Earth sciences
Earth, ocean, space
Entropy
Exact sciences and technology
hyperspectral data
Hyperspectral imaging
Image coding
Information analysis
Infrared imaging
Infrared spectra
Internal geophysics
Kalman filter
Layout
Lossless
lossless compression
Spectral correlation
Spectrometers
Spectroscopy
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Description
Summary:Hyperspectral images exhibit significant spectral correlation, whose exploitation is crucial for compression. In this paper, we investigate the problem of predicting a given band of a hyperspectral image using more than one previous band. We present an information-theoretic analysis based on the concept of conditional entropy, which is used to assess the available amount of correlation and the potential compression gain. Then, we propose a new lossless compression algorithm that employs a Kalman filter in the prediction stage. Simulation results are presented on Airborne Visible Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment, and Hyperspectral Mapper scenes, showing competitive performance with other state-of-the-art compression algorithms.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2008.2009316