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Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features

Weinmann, M.; Jutzi, B. ORCID iD icon; Mallet, C.


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.

Volltext §
DOI: 10.5445/IR/1000073668
DOI: 10.5194/isprsannals-II-3-181-2014
Zitationen: 173
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2014
Sprache Englisch
Identifikator ISSN: 2194-9050
KITopen-ID: 1000073668
Erschienen in ISPRS annals
Verlag Copernicus Publications
Band II-3
Seiten 181–188
Bemerkung zur Veröffentlichung ISPRS Technical Commission III Symposium, 5 – 7 September 2014, Zürich, CH
Schlagwörter LIDAR, Point Cloud, Neighborhood Size Selection, Features, Feature Selection, Classification, Interpretation
Nachgewiesen in Dimensions
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