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Contextual Classification of Point Clouds Using a Two-stage Crf : Volume Xl-3/W2, Issue 1 (10/03/2015)

By Niemeyer, J.

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Book Id: WPLBN0004014843
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Contextual Classification of Point Clouds Using a Two-stage Crf : Volume Xl-3/W2, Issue 1 (10/03/2015)  
Author: Niemeyer, J.
Volume: Vol. XL-3/W2, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


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Heipke, C., Soergel, U., Rottensteiner, F., & Niemeyer, J. (2015). Contextual Classification of Point Clouds Using a Two-stage Crf : Volume Xl-3/W2, Issue 1 (10/03/2015). Retrieved from

Description: Institute of Photogrammetry and GeoInformation, Leibniz Universit¨at Hannover, Germany. 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.



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