Distributed Computing for Efficient Hyperspectral Imaging Using Fully Heterogeneous Networks of Workstations

Hyperspectral imaging is a new technique which has become increasingly important in many remote sensing applications, including automatic target recognition for military and defense/security deployment, risk/hazard prevention and response including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination, etc. Hyperspectral imaging applications generate massive volumes of data and require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of workstations represent a very promising cost-effective solution expected to play a major role in the design of highperformance computing platforms for many on-going and planned remote sensing missions. This paper explores innovative techniques for mapping hyperspectral analysis algorithms onto heterogeneous networks of workstations available at NASA’s Goddard Space Flight Center and University of Maryland. Experimental results reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in hyperspectral imaging studies.