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A statistical approach to preprocess and enhance C-band SAR images in order to detect automatically marine oil slicks / Zhour Najoui in IEEE Transactions on geoscience and remote sensing, vol 56 n° 5 (May 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 5 (May 2018) . - pp 2554 - 2564 Titre : A statistical approach to preprocess and enhance C-band SAR images in order to detect automatically marine oil slicks Type de document : Article/Communication Auteurs : Zhour Najoui , Auteur ; Serge Riazanoff, Auteur ; Benoit Deffontaines , Auteur ; Jean-Paul Xavier, Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 2554 - 2564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] détection automatique [Termes IGN] image radar moirée [Termes IGN] restauration d'image Résumé : (auteur) The aim of this paper was to propose a new methodology for preprocessing and enhancing C-band synthetic aperture radar (SAR) images for the automatic detection of marine oil slicks. The proposed methodology includes three processing levels: preprocessing, thresholding, and binary cleaning. The first level is to correct the heterogeneity of brightness in SAR images caused by the non-Lambertian reflection of the radar signal on the sea surface. This heterogeneity can be justified by: the distance from the nadir (incidence angle effect), the interaction between wind direction and radar pulse, and the wide swath mode. The second level consists of a thresholding step. The third level is to clean the binary output images from noise residues. Several preprocessing and cleaning methods have been tested and evaluated by a qualification engine that compares the automatically detected patches with a training data set of manually detected dark patches. The training data set includes oil slicks and lookalikes. As a result, the “best” preprocessing method that homogenizes the brightness of C-band SAR scenes and optimizes the automatic detection of marine oil slicks is based on an adaptation to the C-band MODel. As for the cleaning process, the tested morphological methods show that small object removal followed by a morphological closing optimizes the automatic detection of marine oil slicks. Numéro de notice : A2018-238 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2760516 Date de publication en ligne : 11/01/2018 En ligne : https://doi.org/10.1109/TGRS.2017.2760516 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90188 [article]