Abstract
Abstract. 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.
Highlights
The classification of airborne LiDAR point clouds is challenging for urban areas due to the large amount of different objects located close to each other
The aim of this paper is to present and investigate a new two-stage Conditional Random Field (CRF) framework, which was inspired by the approaches of Luo and Sohn (2014), Xiong et al (2011), and Albert et al (2014)
The performance of our method is evaluated on the LiDAR benchmark data set of Vaihingen, Germany (Cramer, 2010) from the ’ISPRS Test Project on Urban Classification and 3D Building doi:10.5194/isprsarchives-XL-3-W2-141-2015
Summary
The classification of airborne LiDAR point clouds is challenging for urban areas due to the large amount of different objects located close to each other. Most applications in the related work do not exploit the full potential of graphical models: up to now, they mainly make use of relatively simple models for the interactions such as the Potts model and the contrast-sensitive Potts model (Boykov and Jolly, 2001). Both models favour neighbouring points to have the same object class by penalising label changes. Small objects, such as cars, might be eliminated for this reason. Niemeyer et al (2011) showed that the use of a more complex multi-class model for the joint probability of all class labels at neighbouring sites, rather than a binary model for the probability of the two labels being equal, leads to better results in terms of completeness
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