2021 |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery Article de journal Dans: Journal of the Indian Society of Remote Sensing, vol. 49, no. 6, p. 1245 – 1255, 2021, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: classification, GeoEye, mapping method, NDVI, satellite imagery, spatial resolution, supervised classification, Tunisia, urban area @article{Chairet20211245b,This paper explores the potentiality of using the completed local binary pattern (CLBP) for the classification of an urbanized oasis area situated in southeastern Tunisia, in very high spatial resolution GeoEye imagery. To further improve the spatial information description derived by CLBP, which is successfully used in face recognition, we applied the multi-scale completed local binary pattern (MSCLBP) to classify the single-dated image. A supervised classification framework preceded by mean-shift segmentation is applied using the texture features alone and combined with the normalized difference vegetation index (NDVI). As the segmentation is a crucial step to obtain a good mapping, considerations are given to select the optimal combination of mean-shift input parameters, such as spatial radius and range radius. The results of this study indicate that MSCLBP outperforms the single-scale CLBP and the gray-level co-occurrence matrix (GLCM) descriptors in the task of expressing the classes of interests. © 2021, Indian Society of Remote Sensing. |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery Article de journal Dans: Journal of the Indian Society of Remote Sensing, vol. 49, no. 6, p. 1245 – 1255, 2021, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: classification, GeoEye, mapping method, NDVI, satellite imagery, spatial resolution, supervised classification, Tunisia, urban area @article{Chairet20211245,This paper explores the potentiality of using the completed local binary pattern (CLBP) for the classification of an urbanized oasis area situated in southeastern Tunisia, in very high spatial resolution GeoEye imagery. To further improve the spatial information description derived by CLBP, which is successfully used in face recognition, we applied the multi-scale completed local binary pattern (MSCLBP) to classify the single-dated image. A supervised classification framework preceded by mean-shift segmentation is applied using the texture features alone and combined with the normalized difference vegetation index (NDVI). As the segmentation is a crucial step to obtain a good mapping, considerations are given to select the optimal combination of mean-shift input parameters, such as spatial radius and range radius. The results of this study indicate that MSCLBP outperforms the single-scale CLBP and the gray-level co-occurrence matrix (GLCM) descriptors in the task of expressing the classes of interests. © 2021, Indian Society of Remote Sensing. |
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. |
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
2021 |
Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery Article de journal Dans: Journal of the Indian Society of Remote Sensing, vol. 49, no. 6, p. 1245 – 1255, 2021, (Cited by: 5). |
Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery Article de journal Dans: Journal of the Indian Society of Remote Sensing, vol. 49, no. 6, p. 1245 – 1255, 2021, (Cited by: 5). |
2019 |
Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). |
Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). |