Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features
3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-08-01
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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/II-3/181/2014/isprsannals-II-3-181-2014.pdf |
| Summary: | 3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas
the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive
features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on
the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective
optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four
successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a
detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2
different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene
interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results. |
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| ISSN: | 2194-9042 2194-9050 |