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：2013
卷号：XL-7/W2
页码：155-160
DOI：10.5194/isprsarchives-XL-7-W2-155-2013
出版社：Copernicus Publications
摘要：This paper describes a line-based classification method, which labels TLS point clouds into vertical object, ground, tree and low objects. A local classifier implements labeling task on individual site independently of its neighborhood, the inference of which often suffers from similar local appearance across different object classes. In this paper, we describe an approach using contextual information as postclassification improvement to a local generative classifier. The contextual information is expected to compensate for ambiguity in objects' visual appearance. A generative classifier is produced using Gaussian Mixture Model (GMM), model parameters of which are iteratively optimized with Expectation-Maximization (EM). The model we use to incorporate contextual information is the Conditional Random Field (CRF), which improves the classification results obtained from GMM-EM classifier by incorporating neighborhood interactions among labeled objects as well as local appearance. The proposed method was validated with three TLS datasets acquired from RIEGL LMS-Z390i scanner using cross validation
关键词：Gaussian Mixture Model; Expectation Maximization; Terrestrial Laser Scanning; Conditional Random Field
