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An individual tree-based automated registration of aerial images to LiDAR Data in a forested area / Jun-Hak Lee in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (September 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 9 (September 2016) . - pp 699 - 710 Titre : An individual tree-based automated registration of aerial images to LiDAR Data in a forested area Type de document : Article/Communication Auteurs : Jun-Hak Lee, Auteur ; Gregory S. Biging, Auteur Année de publication : 2016 Article en page(s) : pp 699 - 710 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre (flore) [Termes IGN] Californie (Etats-Unis) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] écosystème forestier [Termes IGN] forêt [Termes IGN] image aérienne [Termes IGN] point d'appui [Termes IGN] superposition d'images Résumé : (Auteur) In this paper, we demonstrate an approach to align aerial images to airborne lidar data by using common object features (tree tops) from both data sets under the condition that conventional correlation-based approaches are challenging due to the fact that the spatial pattern of pixel gray-scale values in aerial images hardly exist in lidar data. We extracted tree tops by using an image processing technique called extended-maxima transformation from both aerial images and lidar data. Our approach was tested at the Angelo Coast Range Reserve on the South Fork Eel River forests in Mendocino County, California. Although the aerial images were acquired simultaneously with the lidar data, the images had only approximate exposure point locations and average flight elevation information, which mimicked the condition of limited information availability about the aerial images. Our results showed that this approach enabled us to align aerial images to airborne lidar data at the single-tree level with reasonable accuracy. With a local transformation model (piecewise linear model), the RMSE and the median absolute deviation (MAD) of the registration were 9.2 pixels (2.3 meters) and 6.8 pixels (1.41 meters), respectively. We expect our approach to be applicable to fine scale change detection for forest ecosystems and may serve to extract detailed forest biophysical parameters. Numéro de notice : A2016-740 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.82.9.699 En ligne : https://doi.org/10.14358/PERS.82.9.699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82275 [article]