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Proceedings Paper

Improving neurol network performance for remote-sensing image classification
Author(s): Ching Zhang
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Paper Abstract

Neural networks can be used as a new type of classifier for multispectral remote sensing data. To achieve efficient and accurate classification, the selection of neural network structures and training parameters are crucial. This research explores suitable neural network models for practical remote sensing image classification. By using a set of techniques, including multispectral image data compression and training parameters selection, complexity of network training phase have been reduced by half and a classification accuracy above 90 percent has been obtained. The neural network using a Back-Propagation model for supervised remote sensing image classification is presented.

Paper Details

Date Published: 26 March 1993
PDF: 10 pages
Proc. SPIE 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II, (26 March 1993); doi: 10.1117/12.142196
Show Author Affiliations
Ching Zhang, Univ. of Waterloo (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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