• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Editorial Staff
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter Telegram
Journal of Mining and Environment
Articles in Press
Current Issue
Journal Archive
Volume Volume 10 (2019)
Volume Volume 9 (2018)
Volume Volume 8 (2017)
Volume Volume 7 (2016)
Volume Volume 6 (2015)
Volume Volume 5 (2014)
Volume Volume 4 (2013)
Issue Issue 2
Issue Issue 1
Volume Volume 3 (2012)
Volume Volume 2 (2011)
Volume Volume 1 (2010)
Mojeddifar, S., Ranjbar, H., Nezamabadipour, H. (2013). Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data. Journal of Mining and Environment, 4(2), 83-96. doi: 10.22044/jme.2013.163
Saeed Mojeddifar; Hojatollah Ranjbar; Hossain Nezamabadipour. "Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data". Journal of Mining and Environment, 4, 2, 2013, 83-96. doi: 10.22044/jme.2013.163
Mojeddifar, S., Ranjbar, H., Nezamabadipour, H. (2013). 'Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data', Journal of Mining and Environment, 4(2), pp. 83-96. doi: 10.22044/jme.2013.163
Mojeddifar, S., Ranjbar, H., Nezamabadipour, H. Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data. Journal of Mining and Environment, 2013; 4(2): 83-96. doi: 10.22044/jme.2013.163

Adaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data

Article 14, Volume 4, Issue 2, Summer 2013, Page 83-96  XML PDF (2125 K)
Document Type: Original Manuscript
DOI: 10.22044/jme.2013.163
Authors
Saeed Mojeddifar 1; Hojatollah Ranjbar2; Hossain Nezamabadipour3
1Ph.D student of mining exploration engineering, Shahid bahonar university.
2Department of Mining Engineering, Shahid Bahonar University of Kerman,Iran,
3Department of Electrical Engineering, Shahid Bahonar University of Kerman, Iran.
Abstract
The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. The major objective of this paper is to investigate the potential of a neuro-fuzzy system in overcoming this problem. The proposed system is applied to the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA), which hosts many areas of porphyry and vein-type copper mineralization. A software program based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed using the MATLAB ANFIS toolbox. The ANFIS program was used to classify Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data based on the spectral properties of altered and unaltered rocks. The ANFIS result was then compared with other classified images based on artificial neural networks (ANN) and the maximum likelihood classifier (MLC). The verification of the results, based on field and laboratory investigations, revealed that the ANFIS method produces a more accurate map of the distribution of alteration than that obtained using ANN or MLC.
Keywords
Mineral exploration; remote sensing; image classification; ANFIS; Hydrothermal Alteration
Statistics
Article View: 3,712
PDF Download: 3,693
Home | Glossary | News | Aims and Scope | Sitemap
Top Top


free web tracker

 Creative Commons License

JME is licensed under a Creative Commons Attribution 4.0 International License.

Journal Management System. Designed by sinaweb.