Random Forests for building detection in polarimetric SAR data

Building detection from Synthetic Aperture Radar (SAR) images states a particular important as well as difficult problem. The high-resolution which is necessary to distinguish single buildings as well as the geometric and di-electric properties of dense urban areas cause most assumptions to fail, that are commonly made in SAR data analysis. This paper proposes the usage of Random Forests for building detection from high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery. Random Forests can handle high-dimensional input and therefore a large set of different features, they are known to lead to good classification performance in terms of robustness and accuracy, and are nevertheless seldomly applied to analysis of PolSAR images in general and building detection in particular. This paper presents first results of Random Forests when applied to a building detection task and shows their successful applicability.