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Proceedings Paper

Real-time lossy compression of hyperspectral images using iterative error analysis on graphics processing units
Author(s): Sergio Sánchez; Antonio Plaza
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Paper Abstract

Hyperspectral image compression is an important task in remotely sensed Earth Observation as the dimensionality of this kind of image data is ever increasing. This requires on-board compression in order to optimize the donwlink connection when sending the data to Earth. A successful algorithm to perform lossy compression of remotely sensed hyperspectral data is the iterative error analysis (IEA) algorithm, which applies an iterative process which allows controlling the amount of information loss and compression ratio depending on the number of iterations. This algorithm, which is based on spectral unmixing concepts, can be computationally expensive for hyperspectral images with high dimensionality. In this paper, we develop a new parallel implementation of the IEA algorithm for hyperspectral image compression on graphics processing units (GPUs). The proposed implementation is tested on several different GPUs from NVidia, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data sets collected over different locations. The proposed algorithm and its parallel GPU implementation represent a significant advance towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.

Paper Details

Date Published: 1 May 2012
PDF: 9 pages
Proc. SPIE 8437, Real-Time Image and Video Processing 2012, 84370G (1 May 2012); doi: 10.1117/12.923834
Show Author Affiliations
Sergio Sánchez, Univ. of Extremadura (Spain)
Antonio Plaza, Univ. of Extremadura (Spain)


Published in SPIE Proceedings Vol. 8437:
Real-Time Image and Video Processing 2012
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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