HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

Abstract : We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.
Complete list of metadata

Cited literature [37 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01010012
Contributor : Andrés Serna Connect in order to contact the contributor
Submitted on : Thursday, June 19, 2014 - 10:22:53 AM
Last modification on : Wednesday, November 17, 2021 - 12:27:12 PM
Long-term archiving on: : Friday, September 19, 2014 - 10:46:41 AM

File

ISPRS2014_Objects.pdf
Files produced by the author(s)

Identifiers

Citation

Andrés Serna, Beatriz Marcotegui. Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2014, 93, pp.243-255. ⟨10.1016/j.isprsjprs.2014.03.015⟩. ⟨hal-01010012⟩

Share

Metrics

Record views

2871

Files downloads

1797