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:2001
卷号:XXXIV-4/W5
页码:73-76
出版社:Copernicus Publications
摘要:The current status in spatial data handling can be characterized in such a way that tremendous efforts have been made to deliver more data in shorter times by using new sensors and the benefits of the internet, while on the other hand a couple of important automatical data processing methods are neither reliable enough nor operational yet. This general statement is also if not in particular valid for acquiring and processing Digital Elevation Mod- els. On one hand, important developments like radar-interferometric sensors (e.g., the Shuttle Radar Topog- raphic Mission, SRTM) or laser scanners (e.g., the operational systems TopoSys, TopScan or ALTM) have become available to the market. On the other hand, important tasks along the data processing chain like the normalization of Digital Surface Models (DSMs) or the extraction of object types using elevation informa- tion very often do not lead to satisfying and reliable results as obtained with human operators. This is the motivation to present a novel region-based, multi-scale approach which can be applied on elevation data for the above mentioned processing tasks. Chapter 2 will describe the underlying methodology in more detail while Chapter 3 will present results using elevation data from laserscanning. Another reason for unsatisfying processing results is the fact that the full information potential which a hu- man or automatical processing system needs for interpretation purposes is not given if only one data source is considered. For instance, laser scanning methods produce "blind data", i.e. no semantic or image informa- tion is associated with the elevation values - in contrast to optical sensors, whereas laser scanners are much better suited for capturing and processing heights, especially in wooded or urban areas. In this context, Chap- ter 4 will show that the proposed region-based methodology can be transferred and is able to handle multi- sensoral data sources