Heterogeneous Parallel Computing in Remote Sensing Applications: Current Trends and Future Perspectives

Abstract

Heterogeneous networks of computers have rapidly become a very promising commodity computing solution, expected to play a major role in the design of high performance computing systems for remote sensing missions. Currently, only a few parallel processing strategies are available in this research area, and most of them assume homogeneity in the underlying computing platform. This paper develops several highly innovative heterogeneous parallel algorithms for information extraction from high-dimensional remotely sensed images, with particular emphasis on target detection and land-cover mapping applications. Experimental results are presented in the context, of a realistic application, using real data collected by NASA's Jet Propulsion Laboratory over the World Trade Center complex in New York City after September 11th, 2001. Parallel performance of the proposed algorithms is discussed using several (fully and partially) heterogeneous networks at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Combined, these parts deliver a snapshot of the state-of-the-art in those areas, and a thoughtful perspective on the potential and challenges of applying heterogeneous computing practices to remote sensing problems.