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Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study / Lei Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4524 - 4533 Titre : Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study Type de document : Article/Communication Auteurs : Lei Wang, Auteur ; K. Andrea Scott, Auteur ; Linlin Xu, Auteur ; David A. Clausi, Auteur Année de publication : 2016 Article en page(s) : pp 4524 - 4533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par réseau neuronal [Termes IGN] eau de fonte [Termes IGN] glace de mer [Termes IGN] iceberg [Termes IGN] image Radarsat [Termes IGN] navigation maritime [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) High-resolution ice concentration maps are of great interest for ship navigation and ice hazard forecasting. In this case study, a convolutional neural network (CNN) has been used to estimate ice concentration using synthetic aperture radar (SAR) scenes captured during the melt season. These dual-pol RADARSAT-2 satellite images are used as input, and the ice concentration is the direct output from the CNN. With no feature extraction or segmentation postprocessing, the absolute mean errors of the generated ice concentration maps are less than 10% on average when compared with manual interpretation of the ice state by ice experts. The CNN is demonstrated to produce ice concentration maps with more detail than produced operationally. Reasonable ice concentration estimations are made in melt regions and in regions of low ice concentration. Numéro de notice : A2016-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2543660 En ligne : https://doi.org/10.1109/TGRS.2016.2543660 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83066 [article]