Parallel Implementation of Hyperspectral Image Processing Algorithms

A. Plaza, D. Valencia, J. Plaza, J. Sanchez-Testal, S. Munoz, S. Blazquez
2006 2006 IEEE International Symposium on Geoscience and Remote Sensing  
High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent
more » ... plosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems. 0-7803-9510-7/06/$20.00
doi:10.1109/igarss.2006.242 dblp:conf/igarss/PlazaVPSMB06 fatcat:3hyv7i362jdf3fdc4dqa4slrxe