Abstract
Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset by segmentation errors. We combine different point cloud segmentation methods to minimise both under- and over-segmentation. We propose a contextual segment-based classification using a Conditional Random Field. Segment adjacencies are represented by edges in the graphical model and characterised by a range of features of points along the segment borders. A mix of small and large segments allows the interaction between nearby and distant points. Results of the segment-based classification are compared to results of a point-based CRF classification. Whereas only a small advantage of the segment-based classification is observed for the ISPRS Vaihingen dataset with 4–7 points/m2, the percentage of correctly classified points in a 30 points/m2 dataset of Rotterdam amounts to 91.0% for the segment-based classification vs. 82.8% for the point-based classification.
Original language | English |
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Pages (from-to) | 354-371 |
Number of pages | 18 |
Journal | ISPRS journal of photogrammetry and remote sensing |
Volume | 128 |
DOIs | |
Publication status | Published - 1 Jun 2017 |
Keywords
- Classification
- CRF
- Point cloud
- Segmentation
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