Parallel Hyperspectral Image Processing on Distributed Multicluster Systems

Provided by: SpiDoL Games
Topic: Big Data
Format: PDF
Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of emerging problems in hyperspectral imaging, there is a growing need for distributed supercomputing using multicluster systems. A well-known manner of obtaining speedups in hyperspectral imaging is to apply data parallel approaches, in which commonly used data structures (e.g., the image cubes) are being scattered among the available compute nodes.

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