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Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery / Camile Sothe in Geocarto international, vol 36 n° 19 ([01/11/2021])  

Public [article]  inGeocarto international > vol 36 n° 19 [01/11/2021] . - pp 2241 - 2259 Titre : Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery Type de document : Article/Communication Auteurs : Camile Sothe, Auteur ; Claudia Maria de Almeida, Auteur ; Marcos Benedito Schimalski, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2241 - 2259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] Brésil [Termes IGN] délimitation [Termes IGN] forêt tropicale [Termes IGN] houppier [Termes IGN] identification de plantes [Termes IGN] image à très haute résolution [Termes IGN] image Worldview [Termes IGN] méthode heuristique [Termes IGN] orthoimage [Termes IGN] segmentation d'image Résumé : (auteur) In the case of tree species delineation with very high spatial resolution (VHR) images, is desirable that each segment corresponds to one individual tree crown (ITC). However, in order to have a segmentation algorithm that generates segments matching to ITCs, its parameters ought to be properly tuned. Aiming to avoid time-consuming trial-and-error procedures associated with this task, some initiatives for the automatic search of segmentation parameters have been developed, such as metaheuristic methods. The objective of this work was to test the automatic tuning of segmentation parameters of three segmentation algorithms for the delineation of ITCs belonging to a native endangered species in a subtropical forest area, comparing this method with the traditional trial-and-error approach. Two datasets (WorldView-2 and an orthoimage) and three segmentation algorithms (multiresolution, mean-shift and graph-based) were tested. For the automatic approach, a hybrid metaheuristic method was applied to accomplish the automatic search of parameters for the segmentation algorithms, while for the trial-and-error, a visual assessment was conducted for each set of parameters tested. Four supervised metrics were used to assess the quality of the segmentation results for the optimization approach and for the final set of parameters chosen in the trial-and-error approach. Results showed that none of the algorithms, datasets or approaches differ too much. The evaluation metrics values were lower, indicating that the reference ITCs polygons matched with the segmentation results. Despite the similar results, the automatic tuning of segmentation parameters proved to be a feasible alternative to reduce the subjectivity and the human effort in the choice of segmentation parameters as compared to the trial-and error approach. Numéro de notice : A2021-765 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1690056 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1690056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98810 [article]