Share Email Print

Proceedings Paper

GPU implementation of JPEG2000 for hyperspectral image compression
Author(s): Milosz Ciznicki; Krzysztof Kurowski; Antonio Plaza
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral and temporal resolution of remotely sensed hyperspectral data sets, fast (onboard) compression of hyperspectral data is becoming a very important and challenging objective, with the potential to reduce the limitations in the downlink connection between the Earth Observation platform and the receiving ground stations on Earth. For this purpose, implementation of hyperspectral image compression algorithms on specialized hardware devices are currently being investigated. In this paper, we develop an implementation of the JPEG2000 compression standard in commodity graphics processing units (GPUs). These hardware accelerators are characterized by their low cost and weight, and can bridge the gap towards on-board processing of remotely sensed hyperspectral data. Specifically, we develop GPU implementations of the lossless and lossy modes of JPEG2000. For the lossy mode, we investigate the utility of the compressed hyperspectral images for different compression ratios, using a standard technique for hyperspectral data exploitation such as spectral unmixing. In all cases, we investigate the speedups that can be gained by using the GPU implementations with regards to the serial implementations. Our study reveals that GPUs represent a source of computational power that is both accessible and applicable to obtaining compression results in valid response times in information extraction applications from remotely sensed hyperspectral imagery.

Paper Details

Date Published: 12 October 2011
PDF: 11 pages
Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 81830H (12 October 2011); doi: 10.1117/12.897386
Show Author Affiliations
Milosz Ciznicki, Poznan Supercomputing and Networking Ctr. (Poland)
Krzysztof Kurowski, Poznan Supercomputing and Networking Ctr. (Poland)
Antonio Plaza, Univ. of Extremadura (Spain)

Published in SPIE Proceedings Vol. 8183:
High-Performance Computing in Remote Sensing
Bormin Huang; Antonio J. Plaza, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?