Discriminating urban environments using multiscale texture and multiple SAR images

This work is an enlarged version of the paper - Discriminating urban environments using multiscale texture and multiple SAR images', presented at WARSD'03.The use of multiscale textural features for urban satellite Synthetic Aperture Radar (SAR) image characterization is introduced. The multiscale nature of urban environments requires that no single scale of analysis is exclusively considered. An accurate texture-based discrimination of land use/land cover classes needs, for instance, the computation of multiscale textural features for a wide range of parameters of the co-occurrence algorithm. The technique proposed in this paper shows how to reduce the full multiscale feature set to a subset, the most suitable for classification using a fuzzy ARTMAP neural network. This is done by analysing the relevance of each feature for this particular classifier by means of the Histogram Distance Index (HDI). We validate the procedure by providing results of the classification of several satellite SAR data of the same urban test site. The results are encouraging. They show the potential of this technique for automatic extraction of the best texture and scale subset, suitable for efficient urban mapping using SAR satellite data.