期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2005
卷号：XXXVI-8/W27
出版社：Copernicus Publications
摘要：The quantitative assessment of pollutants on urban surfaces is of high economical and ecological interest. Nowadays a better part of the rain water from sealed urban surfaces is treated in sewage plants, although this might not be necessary regarding economical aspects and not desirable regarding ecological considerations, because the load of pollutants of the first flush is much higher than in the following run-off. Therefore, the dimensioning of sewage systems and on-site preflooders may be adopted to this observation and costs may be reduced as well as a subsidence of the groundwater table could be prevented, if unpolluted rainwater is discharged to the groundwater. While the focus of the Engler-Bunte-Institute (EBI), chair of water chemistry, is on the chemical analysis of the rain run-offs, the Institute of Photogrammetry and Remote Sensing (IPF) aims at the characterization of urban roof surfaces, namely their geometry (slope, exposition, size) and their surface material. For this purpose hyperspectral data with high spectral resolution and laser scanning data with high geometric resolution are combined to create a detailed map of these surfaces. In Lemp and Weidner (2004) we already presented first results of our approach based on segmentation using eCognition and a previously presented technique from IP F for segmentation of roof surface patches. The classification of materials using eCognition was solely based on the hyperspectral data. In our recent developments we extend this approach by using the slope information, because of the correlation of roof slope and possible surface materials. For the classification we apply eCognition which allows the introduction of this knowledge as well as the use of detailed spectral properties within a fuzzy classification scheme. This increases the separability of classes with similar spectra but different geometrical attributes. The paper presents new aspects of segmentation, classification and results of data analysis, which will be focused on roof surfaces
