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.
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