WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS
We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-05-01
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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/XLII-1-W1/151/2017/isprs-archives-XLII-1-W1-151-2017.pdf |
| Summary: | We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training
set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes,
partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation
can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each
cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We
demonstrate the improvement provided by our method over two publicly-available large-scale data sets. |
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| ISSN: | 1682-1750 2194-9034 |