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Proceedings Paper

Fast parallel algorithms for remote-sensing image classification
Author(s): Fangju Wang
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Paper Abstract

Digital image classification is a computation intensive task. In remote sensing image analysis, a large proportion of computing time is spent on image classification. Reducing the time required for classification may largely improve the efficiency of image analysis. This is especially significant for real-time applications of remote sensing images. Parallel computing provides effective techniques for improving data processing efficiency. In this paper, three parallel classification algorithms for multiple spectral remote sensing images are described. The strategies for the parallel classification are discussed and experimental results are presented and analyzed.

Paper Details

Date Published: 26 March 1993
PDF: 12 pages
Proc. SPIE 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II, (26 March 1993); doi: 10.1117/12.142195
Show Author Affiliations
Fangju Wang, Univ. of Guelph (Canada)


Published in SPIE Proceedings Vol. 1819:
Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II
Mark J. Carlotto, Editor(s)

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