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논문 기본 정보

자료유형
학술저널
저자정보
Bahrami Yousef (Department of Mining and Metallurgy Amirkabir University of Technology Tehran Iran) Hassani Hossein (Department of Mining and Metallurgy Amirkabir University of Technology Tehran Iran) Maghsoudi Abbas (Department of Mining and Metallurgy Amirkabir University of Technology Tehran Iran)
저널정보
한국자원공학회 Geosystem Engineering Geosystem Engineering Vol.24 No.1
발행연도
2021.1
수록면
18 - 30 (13page)

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Economic mineralization is often associated with alterations that are identifiable by remote sensing coupled geological analysis. The present paper aims to investigate the capabilities of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat-8 and Sentinel-2 data to map iron oxide and hydrothermally alteration zones in the Abhar area, NW Iran. To achieve this goal, the principal component analysis (PCA) and two machine learning methods including support vector machine (SVM) and artificial neural network (ANN) were employed. PCA method was carried out on four bands of all data and then the appropriate principal components were selected to map alterations. Due to the high precision of ASTER data within the short-wave infrared range, these data results are more satisfactory compared with Landsat-8 and Sentinel-2 sensors in detecting hydrothermally alterations through the PCA technique. Based on the obtained maps, the performance of all data types was approximately similar in the detection of iron oxide zones. Our desired data were classified by two methods of SVM and ANN. The results of these algorithms were presented as confusion matrix. According to these results, for hydrothermally alterations, ASTER data showed better performance in both SVM and ANN than other datasets by gaining values greater than 90%. These data did not perform well in the iron oxide zones detection, while Landsat-8 and Sentinel-2 have been more successful. For iron oxide, based on confusion matrix, Landsat-8 data have obtained the values of 78% and 79% through SVM and ANN algorithms, respectively, and also Sentinel-2 has acquired the values of 88.11% and 90.55% via SVM and ANN, respectively. Therefore, to map iron oxide zones, Sentinel-2 data are more favorable than Landsat-8 data. In addition, the ANN algorithm in ASTER data has represented the highest overall accuracy and Kappa coefficient with the values of 88.73% and 0.8453, respectively.
#Abhar #alteration zones #Sentinel-2 #ASTER #SVM #ANN

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참고문헌 (61)

참고문헌 신청
Abedi, M. J / 2012 / Support vector machine for multi-classification of mineral prospectivity areas / Computers & Geosciences 46:272~283 google schola Abrams, M. J / 2000 / The advanced spaceborne thermal emission and reflection radiometer(ASTER) : Data products for the high spatial resolution imager on NASA’s Terra platform / International Journal of Remote Sensing 21:847~859 google schola Abrams, M. J / 1977 / Mapping of hydrothermal alteration in cuprite mining district, nevada, using aircraft scanner images for the spectral region 0.46 to 2.34 mm / Geology 5:713~718 google schola Abrams, M. J / 1983 / Remote sensing for porphyry copper deposits in Southern Arizona / Economic Geology 78:591~604 google schola Abrams, M. J / 2004 / ASTEr user handbook, Version 2 / Jet Propulsion Laboratory, California Institute of Technology google schola

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