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Efficient information extraction from hyperspectral imagery using networks of workstations

The rapid development in 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 sensors such as the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of efficient techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. Heterogeneous networks of workstations are a very promising cost-effective parallel computing architecture. Unlike traditional homogeneous parallel platforms, heterogeneous architectures are composed of processors running at different speeds. This heterogeneity results in distributed-memory parallel computing systems created from commodity components that can satisfy specific computational requirements for the Earth and space sciences community. This paper explores techniques for mapping hyperspectral image analysis algorithms onto heterogeneous networks of workstations. Important aspects in algorithm design such as portability, reusability and scalability are illustrated by using homogeneous and heterogeneous parallel computing facilities at NASA’s Goddard Space Flight Center and, European Center for Parallelism of Barcelona, and University of Extremadura in Spain. Hyperspectral image data from the AVIRIS data repository is used in experiments, which reveal that heterogeneous networks of workstations are a source of computational power that is both accessible and applicable to obtaining results quickly enough for practical use in information extraction applications from hyperspectral imagery. KeywordsHyperspectral imaging, Parallel algorithms, Heterogeneous computing, Spatial/spectral analysis.