Publications

@conference{Chairet2019497b,

title = {Features extraction and land cover classification using Sentinel 2 data},

author = {Radhia Chairet and Yassine Ben Salem and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067108312\&doi=10.1109%2fSTA.2019.8717307\&partnerID=40\&md5=bcfc51b9382e64ab682bed56c2eded1f},

doi = {10.1109/STA.2019.8717307},

year  = {2019},

date = {2019-01-01},

journal = {19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2019},

pages = {497 \textendash 500},

abstract = {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.},

note = {Cited by: 6},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chairet2019497,

title = {Features extraction and land cover classification using Sentinel 2 data},

author = {Radhia Chairet and Yassine Ben Salem and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067108312\&doi=10.1109%2fSTA.2019.8717307\&partnerID=40\&md5=bcfc51b9382e64ab682bed56c2eded1f},

doi = {10.1109/STA.2019.8717307},

year  = {2019},

date = {2019-01-01},

journal = {19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2019},

pages = {497 \textendash 500},

abstract = {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.},

note = {Cited by: 6},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}