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Proceedings Paper

Normalized difference vegetation index calculations from JPEG2000-compressed Landsat 7 images
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Paper Abstract

An ongoing problem in remote sensing is that imagery generally consumes considerable amounts of memory and transmittance bandwidth, thus limiting the amount of data acquired. The use of high quality image compression algorithms, such as the wavelet-based JPEG2000, has been proposed to reduce much of the memory and bandwidth overhead; however, these compression algorithms are often lossy and the remote sensing community has been wary to implement such algorithms for fear of degradation of the data. We explore this issue for the JPEG2000 compression algorithm applied to Landsat-7 Enhanced Thematic Mapper (ETM+) imagery. The work examines the effect that lossy compression can have on the retrieval of the normalized difference vegetation index (NDVI). We have computed the NDVI from JPEG2000 compressed red and NIR Landsat-7 ETM+ images and compared with the uncompressed values at each pixel. In addition, we examine the effects of compression on the NDVI product itself. We show that both the spatial distribution of NDVI and the overall NDVI pixel statistics in the image change very little after the images have been compressed then reconstructed over a wide range of bitrates.

Paper Details

Date Published: 18 October 2004
PDF: 12 pages
Proc. SPIE 5561, Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications, (18 October 2004); doi: 10.1117/12.557954
Show Author Affiliations
James F. Scholl, Optical Sciences Ctr./Univ. of Arizona (United States)
Kurtis J. Thome, Optical Sciences Ctr./Univ. of Arizona (United States)
Eustace L. Dereniak, Optical Sciences Ctr./Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 5561:
Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications
Mark S. Schmalz, Editor(s)

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