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：2015
卷号：XL-3/W2
页码：141-148
DOI：10.5194/isprsarchives-XL-3-W2-141-2015
出版社：Copernicus Publications
摘要：In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: in a first step, a point-based CRF is applied. The resulting labellings are then used to generate a segmentation of the classified points using a Conditional Euclidean Clustering algorithm. This algorithm combines neighbouring points with the same object label into one segment. The second step comprises the classification of these segments, again with a CRF. As the number of the segments is much smaller than the number of points, it is computationally feasible to integrate long range interactions into this framework. Additionally, two different types of interactions are introduced: one for the local neighbourhood and another one operating on a coarser scale. This paper presents the entire processing chain. We show preliminary results achieved using the Vaihingen LiDAR dataset from the ISPRS Benchmark on Urban Classification and 3D Reconstruction, which consists of three test areas characterised by different and challenging conditions. The utilised classification features are described, and the advantages and remaining problems of our approach are discussed. We also compare our results to those generated by a point-based classification and show that a slight improvement is obtained with this first implementation
关键词：Classification; Point Cloud; Conditional Random Fields; Contextual; LiDAR; Urban
