Urban area structuring mapping using an airborne polarimetric SAR image

For several years, image classification and pattern recognition algorithms have been developed for the land coverage mapping using radar and multispectral imagery with medium to large pixel size. As several satellites now distribute submetric-pixel and metric-pixel images (for example QUICKBIRD,TERRASAR-X), the research turns to the study of the structure of cities: building structuring, grassy areas, road networks, etc, and the physical description of the urban surfaces. In that context, we propose to underline new potentialities of submetric-pixel polarimetric SAR images. We deal with the characterization of roofs and the mapping of trees. For that purpose, a first analysis based on photo-interpretation and the assessement of several polarimetric descriptors is carried out. Then, an image classification scheme is built using the polarimetric H/alpha-Wishart algorithm, followed by a decision tree. This one is based on the most pertinent polarimetric descriptors and aims at reducing the classification errors. The result proves the potential of such data. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA.