Discriminating urban environments using multi-scale texture and multiple SAR images

In this work we improve a methodology for discriminating urban environments by means of textural features in SAR images. In particular, we introduce multi-scale co-occurrence features and show how the feature set may be chosen as a function of the training set and the mapping classes. Moreover, we provide and compare results obtained by different satellite SAR sensors on the same urban test site, as well as a combination of these sets. Finally, a short analysis of the polarization effects and their importance in this framework of analysis is considered. The results are extremely encouraging, and show the potential of this technique, even if more research is needed to exploit the capabilities of the new generation of low-Earth orbit SAR satellites.