文章基本信息

标题：Clustering of Lidar Data Using Particle Swarm Optimization Algorithm in Urban area
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作者：Farhad Samadzadegan ; Sara Saeedi
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/W8
页码：334-339
出版社：Copernicus Publications
摘要：One of the fundamental steps in the transformation of the LIDAR data into the meaningful objects in urban area involves theirsegmentation into consistent units through a clustering process. Nevertheless, due to the scene complexity and the variety of objectsin urban area, e.g. buildings, roads, and trees, it is clear that a clustering using only a single cue will not suffice. Considering theavailability of additional data sources, like laser range and intensity information in both first and last echo, more information can beintegrated in the clustering process and ultimately into the recognition and reconstruction scheme. Multi dimensionality nature ofLIDAR data with a dense sampling interval in urban area generates a huge amount of information. This amount of information hasproduced a lot of problems for finding global optima in most of traditional clustering techniques. This paper describes the potentialof a Particle Swarm Optimization (PSO) algorithm to find global solutions to the clustering problem of multi dimensional LIDARdata in urban area. It is a kind of swarm intelligence that is based on social-psychological principles and provides insights into socialbehaviour, as well as contributing to engineering applications. By integrating the simplicity of the k-means algorithm with thecapability of the PSO algorithm, this paper presents a robust and efficient clustering method which can overcome the problem oftrapping to local optima of k-means technique. This algorithm successfully applied to clustering of several LIDAR data sets indifferent urban area with different size and complexities. The experimental results demonstrate that PSO based clustering techniqueproduces much better outputs in terms of both accuracy and computation time than other traditional clustering techniques
关键词：Clustering; LIDAR; Particle swarm optimization; Urban Area; Object Extraction