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Proceedings Paper

Misclassification correction in primary local recognition of component images of multichannel remote sensing data
Author(s): Antti Niemistoe; Vladimir V. Lukin; Alexander N. Dolia; Olli P. Yli-Harja; Ilya Shmulevich
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Paper Abstract

We propose an automatic classification procedure for multichannel remote sensing data. The method consists of several stages. An important stage is the correction of misclassifications based on the use of a nonlinear graph-based estimation technique recently introduced by us. The misclassification correction method is optimized by means of a training-based framework using genetic algorithms. It is shown that this provides a considerable improvement in classification accuracy. After primary local recognition and misclassification correction of all component images, an approach to further use the obtained data is considered. At this joint classification stage we introduce novel subclasses like 'common homogeneous region, common edge, small sized object in one or two components, etc. Numerical simulation data as well as real image processing results are presented to confirm the basic steps of remote sensing data classification and the efficiency of the proposed approach.

Paper Details

Date Published: 28 January 2002
PDF: 12 pages
Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454147
Show Author Affiliations
Antti Niemistoe, Tampere Univ. of Technology (Finland)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Alexander N. Dolia, National Aerospace Univ. (Ukraine)
Olli P. Yli-Harja, Tampere Univ. of Technology (Finland)
Ilya Shmulevich, Univ. of Texas/M. D. Anderson Cancer Ctr. (Finland)

Published in SPIE Proceedings Vol. 4541:
Image and Signal Processing for Remote Sensing VII
Sebastiano Bruno Serpico, Editor(s)

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