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：2002
卷号：XXXIV Part 3 A
页码：330-335
出版社：Copernicus Publications
摘要：The filtering of a laser scanner point-cloud to abstract the bald earth has been an ongoing research topic in laser altimetry. To date a number of filters have been devised for extracting DEMs from laser point-clouds. The measure of the performance of these filters is often based on tests against some reference data (rms, ratio of misclassifications vs. correct classifications, etc.,) obtained by photogrammetric measurement or other means. However, measures based on such tests are only global indicators of how the filters may perform. Therefore, when applied to real life applications, based on such measures it is not possible to say with certainty how well a filter has performed. This uncertainty suggests that a method be devised to identify in a point-cloud those regions where a filter may have difficulty classifying points. This done other sources of information can be gathered to clarify the status of points in (difficult) regions. This fits in with the thinking that external sources of data, such as imagery, maps have be used in the filtering of laser scanner point-clouds. However, devising a method as suggested above requires that the reasons for the misclassification of points be first identified. When filtering a point-cloud based on spatial information alone, misclassification arises from three sources, (1) the nature and arrangement of objects and the terrain in a landscape (e.g., terrain, buildings, vegetation, etc.,) (2) the characteristics of the data (resolution, outliers, data gaps, etc.,) and (3) the implementation of filters. In this paper, the first two reasons for misclassification are outlined because they are common to all filtering problems, and an initial attempt at developing a method for identifying regions in a point-cloud where a filter may have trouble in classifying points is described
