Semi-automated Classification of Urban Areas by Means of High Resolution Radar Data

Almost two thirds of the world’s population will live in cities by 2030. Thus, human settlements typify the most dynamic regions on earth. To cope with this development urban planning and management requires up-to-date information about the various processes taking place within the urbanised zones. As radar remote sensing allows for the collection of areal data under almost all weather and environmental conditions it is predestined for a frequent and near-term retrieval of geo-information. In view of the future TerraSAR-X mission the German Remote Sensing Data Center (DFD) researches into the potential use of high resolution radar imagery. In this context our main objective is to develop concepts for a mostly automated detection and analysis of human settlements by means of high resolution SAR data. The studies are based on multi-frequency single-, dualand quadpolarised SAR data recorded by the airborne Experimental Synthetic Aperture Radar (E-SAR) system of the German Aerospace Center (DLR). This paper presents first results of an object-oriented approach towards a semi-automated identification of built-up areas based on single-polarised E-SAR X-band imagery. The basic concept includes an optimised speckle suppression procedure in order to improve and stabilise subsequent image analysis as well as the development of an object-oriented classification scheme for an automated detection of built-up areas from high resolution X-band imagery. The developed concept for a semi-automated extraction of urban areas from single polarised X-band data yields promising results. Built-up areas could be detected with an accuracy of 86%, 85% and 91% for three flight tracks. While the main body of the settlements could be identified with an accuracy of more than 90% inaccuracies were mainly associated with flanking parks, recreation areas and allotments.