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：2009
卷号：XXXVIII-3/W4
页码：133-138
出版社：Copernicus Publications
摘要：Light Detection and Ranging (LIDAR) systems have become a standard data collection technology for capturing object surfaceinformation and 3D modeling of urban areas. Although, LIDAR systems provide detailed valuable geometric information, they stillrequire extensive interpretation of their data for object extraction and recognition to make it practical for mapping purposes. Afundamental step in the transformation of the LIDAR data into objects is the segmentation of LIDAR data through a clusteringprocess. Nevertheless, due to scene complexity and the variety of objects in urban areas, e.g. buildings, roads, and trees, clusteringusing only one single cue will not reach meaningful results. The multi dimensionality nature of LIDAR data, e.g. laser range andintensity information in both first and last echo, allow the use of more information in the data clustering process and ultimately intothe reconstruction scheme. Multi dimensionality nature of LIDAR data with a dense sampling interval in urban applications, providea huge amount of valuable information. However, this amount of information produces a lot of problems for traditional clusteringtechniques. This paper describes the potential of an artificial swarm bee colony optimization algorithm to find global solutions to theclustering problem of multi dimensional LIDAR data in urban areas. The artificial bee colony algorithm performs neighborhoodsearch combined with random search in a way that is reminiscent of the food foraging behavior of swarms of honey bees. Hence, byintegrating the simplicity of the k-means algorithm with the capability of the artificial bee colony algorithm, a robust and efficientclustering method for object extraction from LIDAR data is presented in this paper. This algorithm successfully applied to differentLIDAR data sets in different urban areas with different size and complexities
