Geostatistical analysis of Landsat-TM lossy compression images in a high-performance computing environment

Lluís Pesquer; Ana Cortés; Ivette Serral; Xavier Pons

doi:10.1117/12.896418

12 October 2011 Geostatistical analysis of Landsat-TM lossy compression images in a high-performance computing environment

Lluís Pesquer, Ana Cortés, Ivette Serral, Xavier Pons

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Proceedings Volume 8183, High-Performance Computing in Remote Sensing; 818307 (2011) https://doi.org/10.1117/12.896418
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

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.

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Lluís Pesquer, Ana Cortés, Ivette Serral, and Xavier Pons "Geostatistical analysis of Landsat-TM lossy compression images in a high-performance computing environment", Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 818307 (12 October 2011); https://doi.org/10.1117/12.896418

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KEYWORDS

Image compression

Remote sensing

Image processing

Earth observing sensors

Landsat

Statistical analysis

Environmental sensing

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