The integrated use of optical and InSAR data for urban land-cover mapping

The aim of this study is to classify urban land-cover types using the features derived from optical and spaceborne synthetic aperture radar (InSAR) data sets. For the efficient discrimination of the selected classes, a rule-based algorithm that uses the initial image segmentation procedure based on a minimum distance rule and the constraints on spectral parameters and spatial thresholds is constructed. The result of the rule-based method is compared with the results of a standard supervised classification and it demonstrates a higher accuracy. Overall, the research indicates that the integrated features of the optical and InSAR images can significantly improve the classification of land-cover types and the rule-based classification is a powerful tool in the production of a reliable land-cover map.