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Open AccessFeature PaperArticle

Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data

1
School of Mining Engineering, College of Engineering, University of Tehran, Tehran 143995-7131, Iran
2
Department of Mining Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
3
Institute of Oceanography and Environment (INOS), University Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia
4
EarthByte Group, School of Geosciences, University of Sydney, Sydney, NSW 2006, Australia
5
School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(8), 1261; https://doi.org/10.3390/rs12081261
Received: 12 March 2020 / Revised: 8 April 2020 / Accepted: 14 April 2020 / Published: 16 April 2020
There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration. View Full-Text
Keywords: dimensionality reduction; principal component analysis; independent component analysis; minimum noise fraction; ASTER; hydrothermal alteration; fuzzy logic dimensionality reduction; principal component analysis; independent component analysis; minimum noise fraction; ASTER; hydrothermal alteration; fuzzy logic
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MDPI and ACS Style

Shirmard, H.; Farahbakhsh, E.; Beiranvand Pour, A.; Muslim, A.M; Müller, R.D.; Chandra, R. Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data. Remote Sens. 2020, 12, 1261. https://doi.org/10.3390/rs12081261

AMA Style

Shirmard H, Farahbakhsh E, Beiranvand Pour A, Muslim AM, Müller RD, Chandra R. Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data. Remote Sensing. 2020; 12(8):1261. https://doi.org/10.3390/rs12081261

Chicago/Turabian Style

Shirmard, Hodjat; Farahbakhsh, Ehsan; Beiranvand Pour, Amin; Muslim, Aidy M; Müller, R. D.; Chandra, Rohitash. 2020. "Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data" Remote Sens. 12, no. 8: 1261. https://doi.org/10.3390/rs12081261

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