A shared memory parallel algorithm for hybrid image classification

Authors:
R. D. Phillips;L. T. Watson;R. 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 '07 Proceedings of the 2007 spring simulation multiconference - Volume 2
Year:
2007

Citing 3
Cited 0

Remote Sensing Digital Image Analysis: An Introduction

Remote Sensing Digital Image Analysis: An Introduction
Hybrid image classification and parameter selection using a shared memory parallel algorithm

Computers & Geosciences
Validity of the single processor approach to achieving large scale computing capabilities

AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference

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Abstract

This work presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection) to facilitate the transition from serial to parallel processing. This transition is motivated by a demonstrated need for more computing power driven by the increasing size of remote sensing datasets due to higher resolution sensors, larger study regions, and the like. Parallel IGSCR was developed to produce fast and portable code using Fortran 95 and OpenMP. Parallel results are given using the SGI Altix 3300 shared memory computer and the SGI Altix 3700 with as many as 64 processors reaching speedups of almost 77.