Parallel Detection of Targets in Hyperspectral Images Using Heterogeneous Networks of Workstations

Heterogeneous networks of workstations have rapidly become a cost-effective computing solution in many application areas. This paper develops several highly innovative parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in remote sensing-based homeland security and defense applications. In order to illustrate parallel performance, we consider four (partially and fully) heterogeneous networks of workstations distributed among different locations at University of Maryland, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results indicate that heterogeneous networks can be used as a viable low-cost alternative to homogeneous parallel systems in many on-going and planned remote sensing missions.