Fusing ASTER and QuickBird-2 Satellite Data for Detailed Investigation of Porphyry Copper Deposits Using PCA; Case Study of Naysian Deposit, Iran

Abstract

Hydrothermal alteration mineral zones associated with porphyry copper mineralization can be identified and discriminated based on distinctive spectral properties of the ASTER satellite data. Image processing techniques are mostly used for regional scale mapping but in this study, we aimed to evaluate them for more detailed exploration studies by fusing ASTER and QuickBird-2 satellite data using Gram-Schmidt sharpening method to enhance the spatial resolution of the imagery. Selective principal component analysis (PCA) technique is used to provide mineral distribution maps from the spatially improved ASTER data. PCA was applied to ASTER bands with the objective of mapping the occurrence of mineral end-members related to a porphyry copper deposit named Naysian in Iran. The results illustrate the ability of ASTER data for extracting information on alteration minerals which are valuable for mineral exploration studies and support the role of PCA as a very robust image processing technique for that purpose. The spatial distribution of hydrothermal alteration zones has been verified by insitu field observations. This investigation indicated that applying image processing techniques has a great ability to obtain significant and comprehensive information in detailed stages of porphyry copper deposit exploration at a local scale. The results of this research can assist exploration geologists to make a better planning for more detailed stages of exploring porphyry copper and even gold deposits in other virgin regions before costly detailed ground investigations. Consequently, the introduced image processing approach can present an optimum idea about the application of remote sensing in final stages of mineral exploration.