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USE OF HYPERSPECTRAL AND LASER SCANNING DATA FOR URBAN MATERIAL MAPPING: COMPARISON OF A PIXEL-BASED AND AN OBJECT-BASED CLASSIFICATION APPROACH

Urban areas are characterised by a high heterogeneity of surfaces. In the context of urban surface mapping, hyperspectral imagery proved to be a valuable data source in discriminating different materials. However, there are limitations in the identification of urban surface material types. These are e.g. caused by the fact that some surfaces consist of spectrally similar materials (like street asphalt and bitumen on roofs). Supplementing hyperspectral data with elevation information is used to help separate roof from ground surface materials with similar spectra. Two classification approaches are compared: a pixel-based classification using a binary building mask created from the laser scanning data to distinguish buildings from non-buildings and a hierarchical classification. The latter utilises various information sources for different decisions within a fuzzy classification scheme and is part of a project to quantitatively assess pollutants on urban surfaces by chemical analysis and image processing methods (1, 2). A hyperspectral dataset of high spectral resolution and a DSM from laser scanning data (high geometric resolution) are combined to create a detailed map of roof materials. Results of the project indicate that the additional information derived from height data can help in the classification of urban areas.