Urban land use mapping with multi-spectral and SAR satellite data using neural networks

Statistical, textural and Gabor features were extracted from integrated multitemporal multispectral TM data and ERS-1 SAR imagery for urban land use mapping. The computed features are first normalised using the SOM algorithm and then a decision tree algorithm is applied for feature selection. The classification procedure was carried out with a multilayer perceptron, trained with the resilient backpropagation algorithm. The authors' results demonstrate the potential of the proposed methodology.