Examining the potential parallel scalability of a fuzzy semi-supervised classification algorithm

Authors:
Rhonda D. Phillips;Layne T. Watson;Randolph H. Wynne
Affiliations:
Virginia Polytechnic Institute and State University, Blacksburg, Virginia;Virginia Polytechnic Institute and State University, Blacksburg, Virginia;Virginia Polytechnic Institute and State University, Blacksburg, Virginia
Venue:
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Year:
2009

Citing 5
Cited 0

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High Performance Computing in Remote Sensing

High Performance Computing in Remote Sensing

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Abstract

This work describes a parallel, fuzzy version of the iterative guided spectral class rejection (IGSCR) classification algorithm. Fuzzy IGSCR is a semi-supervised classification algorithm for remote sensing that uses fuzzy clustering to associate a large amount of unlabeled data with a small set of labeled data. The parallel version of fuzzy IGSCR (PFI) is a shared memory parallel algorithm that was implemented on an SGI Altix and speedup results were obtained using as many as 12 processors. Parallel speedup for PFI is almost ideal, indicating PFI will be scalable to much larger parallel computers.