Abstract
The iterative self-organizing data analysis technique algorithm (ISODATA) was implemented over supercomputers Kraken, Keeneland and Beacon to explore scalable and high-performance solutions for image processing and analytics using emerging advanced computer architectures. When 10 classes are extracted from one 18-GB image tile, the calculation can be reduced from several hours to no more than 90 seconds when 100 CPU, GPU or MIC processors are utilized. High-performance scalability tests were further implemented over Kraken using 10,800 processors to extract various number of classes from 12 image tiles totalling 216 gigabytes. As the first geospatial computations over GPU clusters (Keeneland) and MIC clusters (Beacon), the success of this research illustrates a solid foundation for exploring the potential of scalable and high-performance geospatial computation for the next generation cyber-enabled image analytics.
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