2019 |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). Résumé | Liens | BibTeX | Étiquettes: Automation, Classification (of information), Extraction, Features extraction, GLCM, High spatial resolution, Image segmentation, Land cover classification, NDVI, Overall accuracies, Process control, Sentinel 2, supervised classification, Textures @conference{Chairet2019497b,Since 2015, the Sentinel 2 satellite provides a medium to high spatial resolution (10m-30m) images. For studying the land cover of Gabes area, located in the South- East of Tunisia, we exploited the 10 m bands of this satellite. We have tested the supervised classification with the SVM classifier. The classification is preceded by a segmentation step. The spectral data, the vegetation index and the texture metrics (GLCM) are used for training. The best Overall Accuracy OA (92, 12%) is obtained when all the used features are combined. © 2019 IEEE. |
Chetoui, Manel; Aoun, Mohamed 2019, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters @conference{Chetoui201990b,In this paper a new instrumental variables methods based on the Higher-Order-Statistics (fourth order cumulants) are developed for continuous-time system identification with fractional models in the errors in variables context. The fractional orders are supposed known a priori and only the linear coefficients are estimated. The developed algorithms are compared to a fractional fourth order cumulants based least squares algorithm. Their performances are tested through a numerical example in two cases: white and colored noises affecting the input and the output measurements. © 2019 IEEE. |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). Résumé | Liens | BibTeX | Étiquettes: Automation, Classification (of information), Extraction, Features extraction, GLCM, High spatial resolution, Image segmentation, Land cover classification, NDVI, Overall accuracies, Process control, Sentinel 2, supervised classification, Textures @conference{Chairet2019497,Since 2015, the Sentinel 2 satellite provides a medium to high spatial resolution (10m-30m) images. For studying the land cover of Gabes area, located in the South- East of Tunisia, we exploited the 10 m bands of this satellite. We have tested the supervised classification with the SVM classifier. The classification is preceded by a segmentation step. The spectral data, the vegetation index and the texture metrics (GLCM) are used for training. The best Overall Accuracy OA (92, 12%) is obtained when all the used features are combined. © 2019 IEEE. |
Publications
2019 |
Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). |
2019, (Cited by: 5). |
Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). |