CLASSIFICATION OF AERIAL PHOTOGRAMMETRIC 3D POINT CLOUDS
We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-05-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1-W1/3/2017/isprs-annals-IV-1-W1-3-2017.pdf |
| Summary: | We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data
is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud
classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant
increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets
that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques
coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds
containing 10 million points in less than 3 minutes on a desktop computer. |
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| ISSN: | 2194-9042 2194-9050 |