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Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR / Denis Horvat in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 1 – 14 Titre : Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR Type de document : Article/Communication Auteurs : Denis Horvat, Auteur ; Borut Žalik, Auteur ; Domen Mongus, Auteur Année de publication : 2016 Article en page(s) : pp 1 – 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de sensibilité [Termes IGN] classification dirigée [Termes IGN] détection automatique [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] méthode robuste [Termes IGN] morphologie mathématique [Termes IGN] prise en compte du contexte [Termes IGN] végétation [Termes IGN] zone rurale Résumé : (auteur) This paper proposes a new method for the detection of vegetation in LiDAR data. As vegetation points are characterised by non-linear distributions, they are efficiently recognised based-on large errors obtained when applying the local fitting of planar surfaces. In addition, three contextual filters are introduced capable of dealing with those exceptions that do not conform with previous interpretations. Namely, they are designed for detecting overgrowing vegetation, small objects attached to the planar surfaces (such as balconies, chimneys, and noise within the buildings) and small objects that do not belong to vegetation (vehicles, statues, fences). During the validation, the proposed method achieved over 97% correctness as well as completeness of vegetation recognition in rural areas while its average accuracy in urban settings was 90.7% in terms of F1F1-scores. The method uses only three input parameters and allows for efficient compensation between completeness and correctness, without significantly affecting the F1F1-score. Sensitivity analysis of the method also confirmed the robustness against a sub-optimal definition of the input parameters. Numéro de notice : A2016-576 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.02.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.02.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81706 [article]