Characterization of ASTER Data for Mineral Exploration - UTM

2.1 ASTER Satellite DataTwo scenes of ASTER data were used in this study are acquired on July 20, 2007. The imageshave been pre-georeferenced to UTM zone 40 North projection with WGS-84 datum. Both datasets are in level 1B format and are geometric and radiometrically corrected. The crosstalkcorrection was performed to both data sets in this study, aimed at removing the effects of energyoverspill from band 4 into bands 5 and 9 (Kanlinowski and Oliver, 2004). We have done thiscorrection by Cross-Talk correction software and then VNIR and SWIR data re-sampled so thatall 9 bands have the same 15×15 m pixel size by ERMapper 6.4 System. In addition, the VNIR-SWIR regions of Level 1B dataset were also radiometrically calibrated using the InternalAverage Relative Reflection (IARR) method, in order to normalize images to a scene averagespectrum.2.1 Data Image AnalysisDigital image processing techniques have been widely used in the search of mineral depositsusing multispectral remote sensing images. The basic idea is that the spectral information relatedto minerals represents a very small fraction of the total information content of these images.Hence, the useful information is often buried within a vast amount of data, mostly unrelated tothe minerals of interest, and is usually not identifiable unless the data is properly processed. Thetask is therefore perform a systematic selective extraction of the information of interest.There are three widely reported approaches to this task, namely: band ratioing technique; andminimum noise fraction (MNF).2.2.1 Spectral Band RatioingBand ratio techniques are useful in discriminating mineral types and vegetation density in remotesensing image data by suppressing the proportionally constant radiance values in the bands andenhancing the differences (Gupta, 2003; Crosta and Filho, 2003; Rowan and Mars, 2003;Ninomiya, 2003a,b). Ratio images may correlate to one or more surface materials such aslithologic types and vegetation density (Crowley et al., 1989; Sabine, 1997; Gupta, 2003). Theband ratio technique has been widely used to extract hydrothermal mineral information in theanalysis of Landsat MSS, TM and ETM image data (Perry, 2004). Because ASTER has 14spectral channels, more ratio images, and therefore more lithologic indices, more accurate resultscan be derived from ASTER than from Landsat data. The optimal band selection for ratio imagesdepends on the spectral properties of the surface material of interest and its abundance relative toother surface cover types (Sabine, 1999). The spectral bands of the ASTER SWIR subsystemwere designed to measure reflected solar radiation in one band centered at 1.65 μm, and fivebands in the 2.10–2.45 μm region in order to distinguish Al-OH, Fe, Mg-OH, H-O-H, and CO3absorption features. Several investigators have documented identification of specific minerals,such as calcite, dolomite, and muscovite, as well as mineral groups, through analysis of ASTERdata (Rowan and Mars, 2003; Rowan et al., 2003; Mars and Rowan 2006; Sanjeevi, 2008).Al(OH)-bearing minerals such as kaolinite, muscovite, alunite show absorptions in bands 5 and6, as well as calcite in bands 8 and 9 of ASTE data. Ninomiya (2003) looked at spectral features

of different minerals in ASTER data and formulated this index for identification Al(OH)-bearingminerals (OHI) = (Band 7) / [Band6]) × ([Band 4] / [Band 6]). These index was applied to ourASTER Level-1B data sets, as well as simple band ratioing for specific interested mineral(muscovite=7/6 ) and vegetation(3/2) and iron oxides (gossan=4/2).2.2.2 Principal Components AnalysisThe principal component transformation is a multivariate statistical technique that selectsuncorrelated linear combinations (eigenvector loadings) of variables in such a way that eachsuccessively extracted linear combination, or principal component, has a smaller variance (Singhand Harrison 1985). This is a well-known method for lithological and alteration mapping inmetalogenic provinces (Crosta et al, 2003, Crosta and Filho, 2003;Tangestani and Moore 2000,2001,2002; Ranjbar et al. 2004; Gomez et al, 2005; Kargi, 2007; Massironi et al, 2008,Tangestani et al, 2008; Amer et al, 2010). In this study, PCA is performed on ASTER data.2.2.3 Minimum Noise FractionMinimum Noise Fraction analysis identifies the locations of spectral signature anomalies. Thisprocess is of interest to explorationists because spectral anomalies are often indicative ofalterations. The minimum noise fraction (MNF) transform is used to determine the inherentdimensionality of image data to segregate noise in the data and to reduce the computationalrequirements for subsequent processing (Boardman and Kruse, 1995). This method is similar toprincipal component (PC) analyses that have been used for a long time in multispectral imageprocessing, but involves an extra preceding step. The MNF procedure was examined on Resampledata and three sub-system VNIR and SWIR and TIR separately.3. RESULTS AND DISCUSSIONFigures 3 to 4 show the output results of spectral band ratioing, PCA and MNF transformsrespectively. Al(OH)-bearing minerals such as muscovite, kaolinite, alunite and iron oxide andvegetation covers can be extracted using spectral band ratioing. These information are shownwith highest DN values in ASTER satellite data and they are ascoictaed associated withalteration zones around porphyry copper deposits (Fig. 3a). Spectral characteristics of vegetationis main obstacle for alteration zone identifications. It is observed that Fig.3b shows vegetation’serror as bright pixels that is resulted by band ratioing of 3/2. The most important mineral in thealteration zones is muscovite because Phyllic alteration spectral characteristics include muscovitereflectance spectra that exhibits an intense of Al-OH absorption feature. This occurred withinwavelength value of 2.20 μm (ASTER band 6). This result confirmed the study of Mars andRowan 2006. Thus, identification of muscovite by band ratioing of 7/6 can be as good indicatorfor copper mineralization (Fig 3c). This study is similar to Abdelsalam et al., (2000) andTangestani and Moore, (2000). Consequently, Supergene alteration can create extensive ironoxide minerals that is called Gossans. Band ratioing of is useful and 4/2 can recognize the ironoxides.(Fig3d). Spectral transforms involving PCA and MNF, both show similar results andverified band ratioing results especially in, PC4,PC5, PC6 and MNF5, MNF6, MNF7.Fig 4ashows the result as RGB color composite PCA transformation for PC4,PC5, PC6 and fig 4bRGB color composite MNF transformation for MNF5, MNF6, MNF7, respectively.

abcdFig 3. Spectral indicies generated using band ratioing: (a) (OHI) inde , (b) vegetation, (c) muscovite (d) iron oxides(gossan) zoom.4. CONCLUSIONSThis paper has shown that the ASTER imagery application in locating and enhancing alterationzones associated with ore deposits such as gold and cooper. Conventional image processingmethods such as band ratioing, (PCA), (MNF) applied on our ASTER data. These results areabsolutely correspond with maps provided by Geological Survey of Iran and pervious remotesensing investigations that were carried out by Tangestani, and Moore,2001, 2002 on TM dataand Tangestani, et al. 2008 on ASTER data in Meiduk region and Ranjbar et al. 2004 on ETM +data in Sarcheshmeh region and Mars and Rowan,2006 on ASTER data in the Zagros magmaticarc.

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