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Agricultural Fields Classification in Semi-Arid Central Tunisia using SPOT 7 Image Host Publication: 4th International Conference on Control Engineering & Information Technology Authors: R. Mezzi, M. Allani, M. Perez Gonzalez, H. Boukhari, W. Abdallah, F. Stoffner, M. Elyes Hamza, M. Hans Werner, H. Sahli and A. Sahli Publisher: IEEE Publication Date: Dec. 2016 Number of Pages: 4
Abstract: This paper reports on classification methods applied and tested for land use classification in a semi-arid environment. Our study, conducted on two irrigates sites located in the Kairouan region, the largest irrigated region in Tunisia, compared Support Vector Machine (SVM) and Maximum Likelihood classification of SPOTlj data. To produce a per-field classification a Mean-Shift Segmentation has been performed on the pansharpened SPOTlj images. A field survey has been conducted to. Accuracy assessment was done to evaluate the performance of the proposed using collect ground truth data on land use and extend of all the agricultural fields within the study areas obtained through filed survey. External Link.
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