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：2004
卷号：XXXV Part B3
页码：408-413
出版社：Copernicus Publications
摘要：The object classification can play an important role in a lot of applications of airborne laserscanning data. The filtering process and the subsequent DTM generation using airborne laserscanning data can be significantly improved by classification of non-terrain objects (e.g. vegetation, buildings etc.). On the other hand classification can be also the first step of object-specific modelling, like vegetation or building reconstruction for 3D city models, design of telecommunication networks, urban planning or disaster management. A pixel-wise classification – especially when using laserscannning data - is limited in terms of reliability of its results. Therefore, the first step of this approach will be a segmentation of 3D objects. For each segment object-specific features (e.g. height texture, shape etc.) are extracted and used for subsequent classification process. In this phase the method is based on raster data. For segmentation a normalised DSM (nDSM) is generated by subtracting the original laser data (DSM) from a rough DTM (created by a strong filtering of the DSM). Now 3D objects can be segmented by means of specific a region growing algorithm on this nDSM. Different kind of object-oriented features are calculated for each segment, like height texture, border gradients, first/last pulse height differences, shape parameters or laser intensities. For classification two methods have been applied, on one hand a fuzzy logic classification, on the other hand a statistical method (maximum likelihood). The fuzzy logic approach resulted in an overall classification rate of about 95% for test site 'Salem' (hilly terrain) and about 90% for test site 'Karlsruhe' (flat terrain). The confusion matrix for 'Salem' show that buildings were erroneous classified as trees (5%) resp. trees as buildings (4%). The most errors can be observed at terrain objects which are confused mainly with trees (7%). Investigations concerning the statistical approach are currently done. Results and a comparison with fuzzy logic approach will be presented in this paper
