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What is Automated Lithological Classification SVM about?

This study explores the use of support vector machines (SVM) for automatic lithological classification from remote sensing data in northwestern India. By analyzing ASTER imagery, digital elevation models, and aeromagnetic data, the researchers achieved a classification accuracy of 92.34%, outperforming traditional methods like maximum likelihood classifiers. The findings demonstrate the effectiveness of SVM in generating lithological maps for areas with limited geological information.

Author

Zafri Farid

Language

EN