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

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.

[1]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[2]  Mark H. Hansen,et al.  The forest inventory and analysis sampling frame , 2005 .

[3]  Lorenzo Bruzzone,et al.  Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote sensing images , 1999, Remote Sensing.

[4]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[5]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[6]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[7]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[8]  Layne T. Watson,et al.  Hybrid Image Classication Using a Shared Memory Parallel Algorithm , 2005 .

[9]  David M. Rizzo,et al.  Sudden Oak Death , 2003 .

[10]  James Demmel,et al.  LAPACK Users' Guide, Third Edition , 1999, Software, Environments and Tools.

[11]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[12]  David A. Landgrebe,et al.  Partially supervised classification using weighted unsupervised clustering , 1999, IEEE Trans. Geosci. Remote. Sens..

[13]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[14]  J. R. Jensen,et al.  Inland wetland change detection using aircraft MSS data , 1987 .

[15]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[16]  Q. Guo,et al.  A comparison of standard and hybrid classifier methods for mapping hardwood mortality in areas affected by sudden oak death , 2004 .

[17]  William A. Bechtold,et al.  The enhanced forest inventory and analysis program - national sampling design and estimation procedures , 2005 .

[18]  Michael J. Quinn,et al.  Parallel programming in C with MPI and OpenMP , 2003 .

[19]  John A. Scrivani,et al.  Automated Forest Area Estimation Using Iterative Guided Spectral Class Rejection , 2006 .

[20]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[21]  Gita Alaghband,et al.  Fundamentals of Parallel Processing , 2002 .

[22]  Arthur J. Bernstein,et al.  Analysis of Programs for Parallel Processing , 1966, IEEE Trans. Electron. Comput..

[23]  Ronald H. Perrott,et al.  Parallel programming , 1988, International computer science series.

[24]  K. Huang,et al.  A synergistic automatic clustering technique (SYNERACT) for multispectral image Analysis , 2002 .

[25]  Jack Dongarra,et al.  MPI: The Complete Reference , 1996 .

[26]  John A. Scrivani,et al.  Landsat TM-Based Forest Area Estimation Using Iterative Guided Spectral Class Rejection , 2001 .

[27]  William A. Bechtold,et al.  The forest inventory and analysis plot design , 2005 .

[28]  Pol Coppin,et al.  Satellite inventory of Minnesota forest resources , 1994 .