CONTEXTUAL CLASSIFICATION OF POINT CLOUDS USING A TWO-STAGE CRF
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 t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-03-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W2/141/2015/isprsarchives-XL-3-W2-141-2015.pdf |
| Summary: | 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.
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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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| ISSN: | 1682-1750 2194-9034 |