CLUSTERS VERSUS FPGA FOR PARALLEL PROCESSING OF HYPERSPECTRAL IMAGERY
Hyperspectral imaging is a new technique in remote sensing that generates images with hundreds of spectral bands, at different wavelength channels, for the same area on the surface of the Earth. Although in recent years several efforts have been directed toward the incorporation of parallel and distributed computing in hyperspectral image analysis, there are no standardized architectures for this purpose in remote sensing missions. To address this issue, this paper develops two highly innovative implementations of a standard hyperspectral data processing chain utilized, among others, in commercial software tools such as Kodak's Research Systems ENVI software package ( one of the most popular tools currently available for processing remotely sensed data). It should be noted that the full hyperspectral processing chain has never been implemented in parallel in the past. Analytical and experimental results are presented in the context of a real application, using hyperspectral data collected by NASA's Jet Propulsion Laboratory over the World Trade Center area in New York City, shortly after the terrorist attacks of September 11th 2001. The parallel implementations are tested in two different platforms, including Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center, and a Xilinx Virtex-II field programmable gate array (FPGA) device. Combined, these platforms deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel processing systems into realistic hyperspectral imaging problems.
hyperspectral imaging hyperspectral data processing chain commodity clusters reconfigurable computing pixel purity index