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Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3040 - 3053 Titre : Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data Type de document : Article/Communication Auteurs : Xiao-Ming Li, Auteur ; Yan Sun, Auteur ; Qiang Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 3040 - 3053 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Arctique, océan [Termes IGN] classification non dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] entropie [Termes IGN] extraction de traits caractéristiques [Termes IGN] glace de mer [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] polarisation croisée [Termes IGN] rétrodiffusion [Termes IGN] texture d'image Résumé : (auteur) In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical–horizontal (VH) or horizontal–vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%–95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%. Numéro de notice : A2021-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007789 Date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007789 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97392 [article]