Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can...

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Bibliographic Details
Main Authors: Mastoureh Yousefi, Seyed Hassan Tabatabaei, Reyhaneh Rikhtehgaran, Amin Beiranvand Pour, Biswajeet Pradhan
Format: article
Language:EN
Published: MDPI AG 2021
Subjects:
porphyry copper deposits
ASTER
machine learning
DP
SVM
SAM
Mineralogy
QE351-399.2
Online Access:https://doaj.org/article/8592dc01e06644e6a1437f480d9e7e96
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https://doaj.org/article/8592dc01e06644e6a1437f480d9e7e96

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