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Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression

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COMPUTERS & GEOSCIENCES 2010年第10期36卷 1283-1291页

作者： Plaza, Antonio Plaza, Javier Paz, Abel Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab E-10071 Caceres Spain

Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Heterogeneous parallel computing adaptive run-time data compression Wavelet transform Hyperspectral imaging Remote sensing

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