An urban classification approach based on an object-oriented analysis of high resolution satellite imagery for a spatial structuring within urban areas

Abstract

Classifying the complex structures of urban morphology from high resolution remote sensing imagery comprises difficulties due to their spectral and spatial heterogeneity. This paper presents a methodology allowing derivation of area-wide spatial information meaningful for city planning and management from IKONOS imagery. The initial point is a stable segmentation for an object-oriented approach to derive a thematic land cover classification. The classification methodology – which is predominantly based on shape and neighbourhood related features - is exemplified by the extraction of roads with a region-growing rule base. The approach follows the assumption that objects representing real world structures correspond in any urban area. Finally, the urban land cover classification is used to compute a spatial distribution of built-up densities within the city and to map homogeneous zones or structures of urban morphology. The aim apart from the information on urban morphology is to derive indirectly standard socio-economic data for further support of city management and planning. The result shows an allocation of different urban zones within the city of Istanbul with an accuracy of 82% compared to a digitized layer based on visual classification.