Automatic tracking of land cover transformation through Landsat multispectral scanner (MSS) images using neural network classifiers

In this paper, we report an automatic land cover tracking system which is based on a neural network classifier to extract the land cover from multi-temporal satellite images. The neural network classifier has a three-layer feedforward structure. The input layer has several input units for each of the preprocessed spectral bands of the LANDSAT multispectral scanner, one unit for the digital elevation model, and several units for texture features obtained from a 5 by 5 moving window. The output layer has a neuron for each of the land-cover classes. A pixel is classified with the label of the output layer neuron with the largest activation. The proposed approach provides a quick assessment on the land cover transformation for multitemporal satellite images.