Monitoring urban areas by using ERS-SAR data and neural networks algorithms

This contribution discusses the kind of information contained in multitemporal SAR data and shows how it can be exploited for classifying the urban area of Rome, Italy. Multitemporal, coherence and textural features are obtained from a set of SAR images taken in winter, spring and summer by the ERS tandem mission. These features are used to identify areas belonging to various urban classes, including water surfaces, woodland and parks, and continuous high/low density residential areas. The decision-making process is performed by a classifier based on a neural network algorithm