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Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1088 - 1108 Titre : Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Jingfeng Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1088 - 1108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] Chine [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] jeu de données [Termes IGN] polarisation [Termes IGN] rizière [Termes IGN] surface cultivée Résumé : (auteur) SVM and RF are widely used in rice mapping. However, their performance with single and different combinations of satellite datasets is rarely reported. Hence we report their rice mapping accuracies for two seasons using Sentinel-1A, Landsat-8 and Sentinel-2A images. The VH and VV polarizations of Sentinel-1A, and two spectral indices (SIs) of Landsat-8 and Sentine1-2A were used to obtain seven datasets (VH, VV, SI, VHVV, VHSI, VVSI and VHVVSI), and on which SVM and RF were applied and accuracies were assessed. VHSI showed the highest overall accuracy for both algorithms in both years. SVM with VHSI had a slightly higher accuracy (90.8%) than RF with VHSI (89.2%) in 2015 while in 2016 RF with VHSI showed a slightly higher accuracy (95.2%) than SVM with VHSI (93.4%). Although they produced equivalent accuracies within years, RF is more sensitive to additional data, given a 6.0% increase from 2015 to 2016 with VHSI. Numéro de notice : A2020-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1568586 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1568586 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95501 [article]

Developing a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface / Kangyu Zhang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1561 - 1572 Titre : Developing a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface Type de document : Article/Communication Auteurs : Kangyu Zhang, Auteur ; Jingfeng Huang, Auteur ; Lamin R. Mansaray, Auteur ; Qiaoying Guo, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1561 - 1572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] données polarimétriques [Termes IGN] image Sentinel-SAR [Termes IGN] polarimétrie radar [Termes IGN] surface de la mer [Termes IGN] vent [Termes IGN] vitesse Résumé : (Auteur) This paper evaluates the capability of Sentinel-1 VH-polarized synthetic aperture radar signals, involving 738 scenes in the interferometric wide swath (IW) mode, for ocean surface wind speed retrieval using a novel subswath-based C-band cross-polarized ocean model. When compared with in situ measurements, it is observed that wind speed retrieval accuracy varies progressively along swath, with the most accurate wind speed retrievals being derived from subswath 3 [root-mean-square error (RMSE) of 1.82 m · s -1 ], followed by subswath 2 (RMSE of 1.92 m · s -1 ), while subswath 1 showed the lowest retrieval accuracy (RMSE of 2.37 m · s -1 ). The average RMSE of wind speeds retrieved from all the three subswaths is 2.08 m · s -1 under low-to-high wind speed regimes (wind speeds Numéro de notice : A2019-130 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2867438 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2867438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92459 [article]