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Proceedings Paper

Geostatistical analysis of Landsat-TM lossy compression images in a high-performance computing environment
Author(s): Lluís Pesquer; Ana Cortés; Ivette Serral; Xavier Pons
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Paper Abstract

The main goal of this study is to characterize the effects of lossy image compression procedures on the spatial patterns of remotely sensed images, as well as to test the performance of job distribution tools specifically designed for obtaining geostatistical parameters (variogram) in a High Performance Computing (HPC) environment. To this purpose, radiometrically and geometrically corrected Landsat-5 TM images from April, July, August and September 2006 were compressed using two different methods: Band-Independent Fixed-Rate (BIFR) and three-dimensional Discrete Wavelet Transform (3d-DWT) applied to the JPEG 2000 standard. For both methods, a wide range of compression ratios (2.5:1, 5:1, 10:1, 50:1, 100:1, 200:1 and 400:1, from soft to hard compression) were compared. Variogram analyses conclude that all compression ratios maintain the variogram shapes and that the higher ratios (more than 100:1) reduce variance in the sill parameter of about 5%. Moreover, the parallel solution in a distributed environment demonstrates that HPC offers a suitable scientific test bed for time demanding execution processes, as in geostatistical analyses of remote sensing images.

Paper Details

Date Published: 12 October 2011
PDF: 12 pages
Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 818307 (12 October 2011); doi: 10.1117/12.896418
Show Author Affiliations
Lluís Pesquer, Univ. Autònoma de Barcelona (Spain)
Ana Cortés, Univ. Autònoma de Barcelona (Spain)
Ivette Serral, Univ. Autònoma de Barcelona (Spain)
Xavier Pons, Univ. Autònoma de Barcelona (Spain)

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

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