Hybrid image classification and parameter selection using a shared memory parallel algorithm

  • Authors:

  • Rhonda D. Phillips;Layne T. Watson;Randolph H. Wynne

  • Affiliations:

  • Departments of Computer Science, Mathematics, and Forestry,Virginia Polytechnic Institute and State University, USA;Departments of Computer Science, Mathematics, and Forestry,Virginia Polytechnic Institute and State University, USA;Departments of Computer Science, Mathematics, and Forestry,Virginia Polytechnic Institute and State University, USA

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2007

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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 data sets due to higher resolution sensors, larger study regions, and the like. Parallel IGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The intention of this work is to provide an efficient implementation of the established IGSCR classification algorithm. The applicability of the faster parallel IGSCR algorithm is demonstrated by classifying Landsat data covering most of Virginia, USA into forest and non-forest classes with approximately 90% accuracy. 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. Parallel IGSCR allows an analyst to perform and assess multiple classifications to refine parameters. As an example, parallel IGSCR was used for a factorial analysis consisting of 42 classifications of a 1.2GB image to select the number of initial classes (70) and class purity (70%) used for the remaining two images.