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Auteur Jingfeng Huang |
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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])
[article]
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 d'arbres décisionnels
[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 cultivéeRésumé : (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
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1088 - 1108[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)
[article]
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] vitesseRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1561 - 1572[article]Fully polarimetric synthetic aperture radar (SAR) processing for crop type identification / Gang Hong in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 2 (February 2015)
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Titre : Fully polarimetric synthetic aperture radar (SAR) processing for crop type identification Type de document : Article/Communication Auteurs : Gang Hong, Auteur ; Shusen Wang, Auteur ; Junhua Li, Auteur ; Jingfeng Huang, Auteur Année de publication : 2015 Article en page(s) : pp 109 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] cultures
[Termes IGN] décomposition d'image
[Termes IGN] identification automatique
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radarRésumé : (auteur) The target or polarimetric decomposition is widely used to process multi-polarization SAR imagery to establish a correspondence between physical characteristics of interested objects and observed scattering mechanisms. Polarimetric decomposition parameters are used as the basis for developing new classification methods for analyzing polarimetric SAR data. This study proposes to combine two polarimetric decomposition parameters (entropy (H) and angle (α)) derived from the Cloude and Pottier decomposition method and total scattered power (Span) in crop type identification. Support vector machine (SVM) classification algorithm was selected as a classifier to resolve limitations of classifications based on polarimetric decomposition parameters. The advantages of the proposed method are determined by comparing with other commonly used methods based on polarimetric features and the results produced from the coherency matrix, i.e., without target decomposition. Results show that the proposed method is about 10 percent better than other methods based on polarimetric features without Span, and it outperforms the result from the coherency matrix with about 4 percent improvement in the overall accuracy. Numéro de notice : A2015-967 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.2.109 En ligne : https://doi.org/10.14358/PERS.81.2.109 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80025
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 2 (February 2015) . - pp 109 - 117[article]