Improving neurol network performance for remote-sensing image classification

Ching Zhang

doi:10.1117/12.142196

26 March 1993 Improving neural network performance for remote-sensing image classification

Ching Zhang

Proceedings Volume 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II; (1993) https://doi.org/10.1117/12.142196
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

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.

Citation Download Citation

Ching Zhang "Improving neural network performance for remote-sensing image classification", Proc. SPIE 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II, (26 March 1993); https://doi.org/10.1117/12.142196

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