A Comparative Study of SVM and RF Methods for Classification of Alteration Zones Using Remotely Sensed Data | ||
| Journal of Mining and Environment | ||
| مقاله 4، دوره 11، شماره 1، فروردین 2020، صفحه 49-61 اصل مقاله (5.12 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2019.7956.1664 | ||
| نویسندگان | ||
| N. Mahvash Mohammadi؛ A. Hezarkhani* | ||
| Department of Mining and Metallurgy Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. | ||
| چکیده | ||
| Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for alteration classification by applying two new methods of machine learning, including Random Forest and Support Vector Machine. The 14 band ASTER and 19 derivative data layers extracted from ASTER including band ratio and PC imagery, are used as training datasets for improving the results. Comparison of analytical results achieved from the two mentioned methods confirmed that the SVM model has sufficient accuracy and more powerful performance than RF model for alteration classification in the study area. | ||
| کلیدواژهها | ||
| Classification؛ Machine learning؛ Random forest؛ Support Vector Machine؛ Porphyry copper | ||
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آمار تعداد مشاهده مقاله: 1,370 تعداد دریافت فایل اصل مقاله: 2,234 |
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