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The document presents a comparative analysis of various classifiers applied to the fusion of Sentinel-1 and Sentinel-2 datasets for agricultural land classification. The research highlights the effectiveness of the Support Vector Machine (SVM) classifier, achieving an accuracy of 95.67%, while the Minimum Distance Classifier (MDC) and Maximum Likelihood Classifier (MLC) showed lower accuracies of 91.70% and 89.52%, respectively. The study emphasizes the importance of image fusion in enhancing land cover inf

Author

arijach9

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EN