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 |
|---|---|
| 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 |
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Keywords
- Classification
- CRF
- Point cloud
- Segmentation
- ITC-ISI-JOURNAL-ARTICLE
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Contextual segment-based classification of airborne laser scanner data. / Vosselman, George; Coenen, Maximilian; Rottensteiner, Franz.
In: ISPRS journal of photogrammetry and remote sensing, Vol. 128, 01.06.2017, p. 354-371.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Contextual segment-based classification of airborne laser scanner data
AU - Vosselman, George
AU - Coenen, Maximilian
AU - Rottensteiner, Franz
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
KW - Classification
KW - CRF
KW - Point cloud
KW - Segmentation
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1016/j.isprsjprs.2017.03.010
UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2017/isi/vosselman_con.pdf
U2 - 10.1016/j.isprsjprs.2017.03.010
DO - 10.1016/j.isprsjprs.2017.03.010
M3 - Article
AN - SCOPUS:85018625462
VL - 128
SP - 354
EP - 371
JO - ISPRS journal of photogrammetry and remote sensing
JF - ISPRS journal of photogrammetry and remote sensing
SN - 0924-2716
ER -