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Semi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)  

Public [article]  inMarine geodesy > Vol 43 n° 4 (July 2020) . - pp 376 - 395 Titre : Semi-automatic identification of submarine pipelines with synthetic aperture sonar Images Type de document : Article/Communication Auteurs : Victor Hugo Fernandes, Auteur ; Nilcilene Das Graças Medeiros, Auteur ; Dalto Domingues Rodrigues, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 376 - 395 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] canalisation [Termes IGN] détection de contours [Termes IGN] filtrage du bruit [Termes IGN] scène sous-marine [Termes IGN] sonar Résumé : (Auteur) Synthetic Aperture Sonar (SAS) is a sensor that was designed for hydrographic survey of the seabed. It detects small targets, enabling high geometric resolution images. However, the images generated by SAS are susceptible to speckle noise, which makes digital processing difficult, since the noises are confused with the targets of interest. The goal of this study was to develop a semi-automatic routine for SAS image processing to verify the structural integrity of submarine pipelines. The method presented incorporates four stages: pre-processing to reduce noise and highlight the targets of interest; extraction of features aiming to recognize features related to the pipelines; post-processing to reduce the fragmentation generated during feature extraction; validation to quantify the results from the reference images to estimate the performance of the proposed methodology. The results showed that more than 80% of the submarine pipelines were mapped in the semi-automatic mode, which considerably reduced the time needed to manually identify a large number of pipelines operating on offshore oilfields. Numéro de notice : A2020-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1755916 Date de publication en ligne : 25/04/2020 En ligne : https://doi.org/10.1080/01490419.2020.1755916 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95362 [article]