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Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models / Asli Ozdarici-Ok in Geo-spatial Information Science, vol 26 n° inconnu ([01/08/2023])  

Public [article]  inGeo-spatial Information Science > vol 26 n° inconnu [01/08/2023] Titre : Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models Type de document : Article/Communication Auteurs : Asli Ozdarici-Ok, Auteur ; Ali Ozgun Ok, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] Pinus pinea [Termes IGN] semis de points [Termes IGN] Turquie Résumé : (auteur) Stone Pine (Pinus pinea L.) is currently the pine species with the highest commercial value with edible seeds. In this respect, this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models (DSMs) generated through an Unmanned Aerial Vehicle (UAV) mission. We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information. Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya, Turkey. A Hand-held Mobile Laser Scanner (HMLS) was utilized to collect the reference point cloud dataset. Our findings confirm that the proposed methodology, which uses a single DSM as an input, secures overall pixel-based and object-based F1-scores of 88.3% and 97.7%, respectively. The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm (less than 4 pixels), demonstrating the effectiveness and robustness of the proposed methodology. Finally, the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context. Numéro de notice : A2022-620 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2022.2090864 Date de publication en ligne : 21/07/2022 En ligne : https://doi.org/10.1080/10095020.2022.2090864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101364 [article]

Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 557-569 Titre : Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering Type de document : Article/Communication Auteurs : Ali Ozgun Ok, Auteur ; Asli Ozdarici-Ok, Auteur Année de publication : 2020 Article en page(s) : pp 557-569 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] Citrus limon [Termes IGN] détection de contours [Termes IGN] état de l'art [Termes IGN] extraction d'arbres [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] modèle stochastique [Termes IGN] Turquie Résumé : (Auteur) In this study, we present an original unified strategy for the precise extraction of individual citrus fruit trees from single digital surface model (DSM) input data. A probabilistic method combining the circular shape information with the knowledge of the local maxima in the DSM has been used for the detection of the candidate trees. An active contour is applied within each detected region to extract the borders of the objects. Thereafter, all extracted objects are seamlessly divided into clusters considering a new feature data set formed by (1) the properties of trees, (2) planting parameters, and (3) neighborhood relations. This original clustering stage has led to two new contributions: (1) particular objects or clustered structures having distinctive characters and relationships other than the citrus objects can be identified and eliminated, and (2) the information revealed by clustering can be used to recover missing citrus objects within and/or nearby each cluster. The main finding of this research is that a successful clustering can provide valuable input for identifying incorrect and missing information in terms of citrus tree extraction. The proposed strategy is validated in eight test sites selected from the northern part of Mersin province of Turkey. The results achieved are also compared with the state-of-the-art methods developed for tree extraction, and the success of the proposed unified strategy is clearly highlighted. Numéro de notice : A2020-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.557 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.557 Format de la ressource électronique : LUR article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95933 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2020091	SL	Revue	Centre de documentation	Revues en salle	Disponible