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Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)  

Public [article]  inRemote sensing > vol 12 n° 13 (July-1 2020) . - n° 2165 Titre : Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks Type de document : Article/Communication Auteurs : Hugo Boulze , Auteur ; Anton Korosov, Auteur ; Julien Brajard, Auteur Année de publication : 2020 Projets : 3-projet - voir note / Article en page(s) : n° 2165 Note générale : bibliographie This work was supported by the French Service Hydrographique et Océanographique de la Marine (SHOM) under SHOMImpSIM Project, 111222. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] glace de mer [Termes IGN] image Sentinel-SAR [Termes IGN] texture d'image Résumé : (auteur) A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available. Numéro de notice : A2020-873 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12132165 Date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.3390/rs12132165 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99657 [article]