Parallel Implementation of Target and Anomaly Detection Algorithms for Hyperspectral Imagery
This paper develops several parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in many remote sensing applications. In order to illustrate parallel performance of the proposed parallel algorithms, we consider a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results, collected by the AVIRIS sensor over the World Trade Center, just five days after the terrorist attacks, indicate that commodity cluster computers can be used as a viable tool to increase computational performance of hyperspectral target detection applications.