Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran

Yousefi, M; Tabatabaei, SH; Rikhtehgaran, R; Pour, AB; Pradhan, B

Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran

Yousefi, M Tabatabaei, SH Rikhtehgaran, R Pour, AB Pradhan, B

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Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Geocarto International, 2022, 37, (25), pp. 9788-9816
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Yousefi, M
dc.contributor.author	Tabatabaei, SH
dc.contributor.author	Rikhtehgaran, R
dc.contributor.author	Pour, AB
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2023-04-03T02:32:30Z
dc.date.available	2023-04-03T02:32:30Z
dc.date.issued	2022-01-01
dc.identifier.citation	Geocarto International, 2022, 37, (25), pp. 9788-9816
dc.identifier.issn	1010-6049
dc.identifier.issn	1752-0762
dc.identifier.uri	http://hdl.handle.net/10453/169062
dc.description.abstract	Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	Geocarto International
dc.relation.isbasedon	10.1080/10106049.2022.2025917
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-03T02:32:29Z
pubs.issue	25
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	25

Abstract:

Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.

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http://hdl.handle.net/10453/169062