Identifying man-made objects along urban road corridors from mobile LiDAR data
This letter is dedicated to a generic approach for the automated detection and classification of man-made objects in urban corridors from point clouds acquired by vehicle-borne mobile laser scanning (MLS). The approach is designed based on a priori knowledge in urban areas: 1) man-made objects featu...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2014
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| In: |
IEEE geoscience and remote sensing letters
Year: 2013, Jahrgang: 11, Heft: 5, Pages: 950-954 |
| DOI: | 10.1109/LGRS.2013.2283090 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: http://dx.doi.org/10.1109/LGRS.2013.2283090
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| Verfasserangaben: | Hongchao Fan, Wei Yao, and Long Tang |
| Zusammenfassung: | This letter is dedicated to a generic approach for the automated detection and classification of man-made objects in urban corridors from point clouds acquired by vehicle-borne mobile laser scanning (MLS). The approach is designed based on a priori knowledge in urban areas: 1) man-made objects feature geometric regularity such as vertical planar structures (e.g., building facades), whereas vegetation reveals huge diversity in shape and point distribution and 2) different types of urban man-made objects can be characterized by the point extension and the height above the ground level. Therefore, MLS-based point clouds are first divided into three layers with respect to the vertical height. In each layer, seed points of man-made objects are indicated by a line filter in the footprints of off-ground objects, which is generated by binarizing the spatial accumulation map of the point clouds. These seed points are further classified by examining in which layers the seed points of objects are found. Finally, points belonging to respective objects can be retrieved based on the classified seed points. The experiments show that various man-made objects on both sides of the street can be well detected, with a detection rate of up to 83%. For the classification of detected urban objects, overall accuracy of 92.37% can be achieved. |
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| Beschreibung: | Gesehen am 14.09.2020 First published: 30 October 2013 |
| Beschreibung: | Online Resource |
| DOI: | 10.1109/LGRS.2013.2283090 |