Commodity cluster-based parallel processing of hyperspectral imagery |
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Affiliation: | 1. College of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China;2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;1. Department of Electrical and Electronics Engineering, Hacettepe University, Beytepe Campus, Ankara 06800, Turkey;2. ANDRO Computational Solutions, Rome, NY 13440, USA;3. Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA;4. Army CERDEC, Aberdeen Proving Ground, MD, USA;1. School of Computer Science and Technology, Shandong Provincial Key Laboratory of Software Engineering, Shandong University, Shandong University Software Campus, 1500 Shunhua Road, Jinan, 250101, China;2. NetPEN — Networks and Performance Engineering Research Group, Informatics Research Institute (IRI), University of Bradford, Bradford, BD7 1DP, West Yorkshire, UK |
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Abstract: | The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery. |
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