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Framework for automatic coral reef extraction using Sentinel-2 image time series / Qizhi Zhang in Marine geodesy, vol 45 n° 3 (May 2022)  

Public [article]  inMarine geodesy > vol 45 n° 3 (May 2022) . - pp 195 - 231 Titre : Framework for automatic coral reef extraction using Sentinel-2 image time series Type de document : Article/Communication Auteurs : Qizhi Zhang, Auteur ; Jian Zhang, Auteur ; Liang Cheng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 195 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage de points [Termes IGN] filtrage spatiotemporel [Termes IGN] image Sentinel-MSI [Termes IGN] mesure de similitude [Termes IGN] nébulosité [Termes IGN] récif corallien [Termes IGN] série temporelle Résumé : (auteur) Using supervised and unsupervised classification on a single image to extract coral reef extent results in missing data and wrong extraction results. To improve the accuracy of coral reef extraction, this study proposes a novel technical framework for automatic coral reef extraction based on an image filtering strategy and spatiotemporal similarity measurements of pixel-level Sentinel-2 image time series. This method was applied to the Anda Reef, Daxian Reef, and Nanhua Reef, China, using 1464 Sentinel-2 images obtained from 2015–2020. Sentinel-2 images were automatically selected considering space, time, cloud cover, and image entropy after atmospheric correction. With the binary classification measurement standard using the digitization coral reef results of the Sentinel-2 images as the true value, the time series established by the modified normalized difference water index demonstrated high robustness and accuracy. Analyzing the time series curves of the coral reef and deep water verified that the spatiotemporal similarity measurement of this framework can stably extract the boundaries of the coral reef. Numéro de notice : A2022-353 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/01490419.2022.2051648 Date de publication en ligne : 28/03/2022 En ligne : https://doi.org/10.1080/01490419.2022.2051648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100550 [article]