Abstract
Land cover and crop-types classification are of great importance for monitoring agricultural production and land-use patterns. Many classification approaches have used different parameters settings. In this paper, we investigate the modern classifiers using the most effective parameters to improve the classification accuracy of the major crops and land covers that exist in Sentinel-2 images for Fayoum region of Egypt. Four major crop-types and four major land-cover types are classified. This paper investigates the k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) supervised classifiers. The experimental results show that the SVM and the RF report more robust results. The k-NN reports the least accuracy especially for crop types. The RT, K-NN, ANN, and SVM record 92.7%, 92%, 92.1% and 94.4% respectively. The SVM classifier out-performs the k-NN, ANN and RF classifiers.
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Acknowledgments
This work was supported in part by the GEF/World Bank Project “Regional Co-ordination for Improved Water Resources Management and Capacity Building” alongside The National Authority for Remote, Sensing and Space Science, Egypt.
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Laban, N., Abdellatif, B., Ebeid, H.M., Shedeed, H.A., Tolba, M.F. (2018). Improving Land-Cover and Crop-Types Classification of Sentinel-2 Satellite Images. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_44
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DOI: https://doi.org/10.1007/978-3-319-74690-6_44
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