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bandwidth and 30 m spatial resolution (Liao et al., 2000).These spectral bands could provide abundant informationabout many important earth-surface minerals. The VNIRregion is useful for discriminating minerals exposed at gos-sans, such as goethite, hematite and jarosite. The SWIRregion, on the other hand, covers spectral features of

Corresponding author. Fax: +98 3426226617.E-mail addresses: mh.hosseinjani@gmail.com (M. Hosseinjani Zadeh),

tangestani@susc.ac.ir (M.H. Tangestani), francisco.velasco@ehu.es (F.V.Roldan), i.yusta@ehu.es (I. Yusta).1 Fax: +98 7112284572.2 Fax: +34 946013500.

Available online at www.sciencedirect.com

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Advances in Space Research 51. Introduction

Hyperspectral remote sensing acquires reectance oremittance data in many contiguous spectral bands suchthat for each pixel a complete spectrum can be derivedfrom the covered wavelength region. The advent of air-borne and space-borne hyperspectral remote sensing sen-sors which provide near-laboratory quality reectancespectra indicates a new era of remote sensing.

Airborne hyperspectral data have been available toresearchers since the early 1980s and their importance for

mineralmapping and lithological discrimination arewell doc-umented (Kruse, 1988; Kruse et al., 1990, 1993, 2003; Rowanet al., 2000; 2004; VanDerMeer, 2000; VanRuitenbeek et al.,2006; Van Der Meer et al., 2012). However, acquiring thesedatasets is often very dicult, incur relatively high cost perdata acquisition, and have limited availability.

Launch of the EO-1 in November 2000 introducedhyperspectral sensing of the Earth from space throughthe Hyperion system. Hyperion has a single telescope andtwo spectrometers in visible near-infrared (VNIR) andshort-wave infrared (SWIR) covering the 4002500 nmwith 242 spectral bands at approximately 10 nm spectralAbstract

The main aim of the present study was to examine the feasibility of the EO-1 Hyperion data in discriminating and mapping diagnosticalteration minerals around porphyry copper deposits (PCDs), veried by eld surveys and laboratory analyses. A partial sub-pixelmethod, mixture tuned matched ltering (MTMF), was implemented on a pre-processed and calibrated Hyperion dataset. The testedarea is situated at the Central Iranian Volcano-Sedimentary Complex, where abundant porphyry copper deposits like Sarcheshmeh, Dar-rehzar, and Sereidun are located. The characteristic alteration minerals identied by Hyperion data included biotite, muscovite, illite,kaolinite, goethite, hematite, jarosite, pyrophyllite, and chlorite. Discrimination of these minerals especially biotite and iron oxide (hema-tite and goethite) can provide valuable evidences for PCD exploration projects. Results revealed that Hyperion data prove to be powerfulin discriminating and mapping various types of alteration zones while the data were subjected to adequate pre-processing. 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: Hyperspectral remote sensing; Image processing; EO-1 Hyperion; MTMF; Porphyry copper; AlterationSub-pixel mineral mappingusing EO-1 H

Mahdieh Hosseinjani Zadeh a,, Majid H. TInaki Y

aDepartment of Ecology, Institute of Science and High Technology and EnvibDepartment of Earth Sciences, Faculty of S

cDepartamento de Mineraloga y Petrologa, Facultad de Ciencia y Tecnolog

Received 23 April 2013; received in revised forAvailable online0273-1177/$36.00 2013 COSPAR. Published by Elsevier Ltd. All rights resehttp://dx.doi.org/10.1016/j.asr.2013.11.029

Downloaded From http://www.elearnica.irf a porphyry copper beltperion data

ngestani b,1, Francisco Velasco Roldan c,2,sta c,2

ental Science, Graduate University of Advanced Technology, Kerman, Iran

nces, Shiraz University, Shiraz, 71454, Iran

niversidad del Pas Vasco (UPV/EHU), Apdo. 644, Bilbao E-48080, Spain

November 2013; accepted 14 November 2013November 2013

www.elsevier.com/locate/asr

ect

3 (2014) 440451rved.

s inhydroxyl-bearing minerals as well as CO bearing mineralslike phylosilicates, sulfates, and carbonates which are com-mon to many geologic rock units and hydrothermal alter-ation assemblages (Hunt, 1977; Hunt and Ashley, 1979).This region provides spectral information about most ofdiagnostic altered minerals such as biotite, sericite, illite,kaolinite, alunite, pyrophyllite, chlorite, calcite, epidote,and jarosite, (Hunt, 1977; Hunt and Ashley, 1979; Yangand Huntington, 1996; Clark, 1999; Rowan et al., 2004).Since these minerals are indicators of dierent alterationzones, their identication can provide direct evidence ofmineralization. Multispectral sensors such as ASTER typ-ically contain sucient spectral information for the suc-cessful discrimination of general alteration zonesincluding, phyllic, argillic and propylitic (Zhang et al.,2007; Gabr et al., 2010; Mars and Rowan, 2010; Bedini,2011; Hosseinjani Zadeh and Tangestani, 2011; Ranjbaret al., 2011; Amer et al., 2012). However, the spectral char-acteristics of individual minerals such as biotite, goethiteand hematite are not always distinct enough to allow fortheir condent determination using broadband multispec-tral datasets such as ASTER or Landsat ETM+. On theother hand, hyperspectral sensors have served to signi-cantly improve the achievable detail of detected minerals.These sensors enable the identication and mapping ofdetailed surface mineralogy due to higher spectralresolution.

Researchers have been involved in eorts to evaluate,validate, and demonstrate Hyperion applications for geo-logic mapping in a variety of sites around the world, espe-cially in the United States, Australia, and South America(Cudahy et al., 2001; Crowley et al., 2003; Hubbardet al., 2003; Kruse et al., 2003; Hubbard and Crowley,2005; Gersman et al., 2008; San and Suzen, 2010; Beiranv-and Pour and Hashim, 2011; Bishop et al., 2011). Most ofthese studies have used Hyperion data for mapping hydro-thermal altered rocks associated with volcanic systems andacid sulfate hydrothermal systems. Although porphyrydeposits are the most important copper resources in theworld, less attention is paid to discriminating alterationzones and gossans around these types of deposits. Idealporphyry copper deposits are usually characterized byhydrothermal alteration zones (Lowell and Guilbert,1970), with the core of potassic-biotitic, surrounded byphyllic, argillic, and propylitic zones. Dierentiationbetween these zones, especially phyllic and potassic -asindicators of high economic potential- is important in por-phyry copper exploration. In general, rare publications areavailable for mapping alteration minerals using Hyperionon the PCDs (Beiranvand Pour and Hashim, 2011; Bishopet al., 2011); hence, more investigations are needed todetermine the feasibilities of this instrument for mappingsuch minerals. Despite the high spectral resolution ofHyperion, not all the alteration minerals are still mappedby remote sensing geologists, and none of the cited studies

M. Hosseinjani Zadeh et al. / Advancedeal with discrimination of potassic-biotitic zone, which ismal alteration was predominantly potassic and propylitic,followed by phyllic, silicic and argillic alterations (Hez-arkhani, 2006). All these zones are arranged with respectto their spatial distribution from the center of the depositto the periphery including potassic, potassic aected byphyllic, strongly phyllic, and propylitic alterations (Shaeiand Shahabpour, 2012).

The Sereidun prospect mainly consists of an Eocene vol-canic complex which is composed of andesite-basalt lava(80%), trachy-andesite lava (10%) and sediments (10%)(Shahabpour, 1982). The Miocene bodies which composedof granodiorite to diorite rocks intruded into the Eocenevolcanic complex. They extensively developed througha good indicator of high-economic potential for coppermineralization.

This study focuses on investigating the ability of Hype-rion data for mapping alteration minerals in southeasterncopper belt of Iran; an area with low vegetation and ade-quate rock exposures. A partial sub-pixel mapping of alter-ation minerals was implemented using the mixture tunedmatched ltering (MTMF) method, coupled with a com-prehensive eld survey and laboratory analyses. This isthe rst evaluation of hyperspectral imagery for detectionand mapping potassic-biotitic zone in porphyry copperdeposits.

2. Geology and mineralization

The study area is situated at the southern part of thecentral Iranian Uromiyeh-Dokhtar magmatic arc, south-eastern Kerman province, Iran (Fig. 1(a)). This magmaticbelt has considerable economic potential for porphyry cop-per mineralization. The largest porphyry copper mine ofIran, Sarcheshmeh, and two other porphyry deposits, Dar-rehzar, and Sereidun, are located at this area (Fig. 1(b)).

Sarcheshmeh which is hosted by a diorite to granodio-rite stock (Waterman and Hamilton, 1975) locates160 km southwestern Kerman. This deposit contains 1200Mt of ore with 0.69% Cu and 0.03% Mo (Shahabpour,2000). The oldest host rocks belong to an Eocene volcano-genic complex known as the Sarcheshmeh complex. Thecomplex consists of pyroxene trachybasalt, pyroxenetrachyandesite, less abundant andesite and rare occur-rences of agglomerate, tu, and tuaceous sandstone.The Eocene volcanogenic complex was intruded by a com-plex series of OligoMiocene granitoid intrusive phasessuch as quartz diorite, quartz monzonite and granodiorite(Atapour and Aftabi, 2007). Hydrothermal alteration andmineralization at Sarcheshmeh are centered on the stock,which were broadly synchronous with its emplacement.The concentric alteration zones from the center to outwardare potassic, biotitic, phyllic, argillic, and propylitic(Waterman and Hamilton, 1975) (Fig. 2(a)). This patternis the same as typical alteration enveloping other porphyryCu deposits (Lowell and Guilbert, 1970). Early hydrother-

Space Research 53 (2014) 440451 441the south and east and overlain by the porphyritic dacitic

s in442 M. Hosseinjani Zadeh et al. / Advancelava. Geothermal deposits of travertine are formed tothe north and northwest of the Sereidun district. The alter-ation types at Sereidun prospect are manifested by earlychloriteepidote (propylitic), transitional quartzsericite(phyllic), quartzclay (argillic), late quartzalunitepyrophyllite (advanced argillic), and quartz pyrophyllite(silicic) (Fig. 2(b)). The phyllic alteration zone is extensively

Fig. 1. (a) Geographical location of the study area in Iran; (b) GeologicalGeological Survey of Iran, 1973b,c).

Fig. 2. (a) Alteration pattern in Sarcheshmeh deposit (modied from Watermcopper prospect (modied from Barzegar, 2007).Space Research 53 (2014) 440451developed throughout the prospecting rocks with dissemi-nated advanced argillic and argillic alterations boundedby propylitized altered rocks exposed at east, south andwest of the area (Barzegar, 2007).

The Darrehzar porphyry copper deposit is situated 8 kmsoutheastern Sarcheshmeh. The ore reserve in Darrehzarhas been estimated to be about 67 Mt at an average copper

map of the study area and locations of copper deposits (modied from

an and Hamilton, 1975); (b) hydrothermal alteration map of the Sereidun

s ingrade of 0.37% (NICICO, 2008). Mineralization in thisarea is associated with diorite and granodiorite, and theirenclosing Eocene volcano-sedimentary rocks. Both intru-sions and their host rocks are extensively altered by hydro-thermal uids into potassic, phyllic, propylitic and argillicassemblages. The altered rocks are relatively oval shapedelongated eastwest with about 2.2 km long and 0.71 kmwide. Hydrothermally altered rocks are highly fractured,and supergene alteration has produced large amounts oflimonite, extensive oxidation, and leaching of suldes, giv-ing a characteristic reddish or yellowish color to the alteredrocks. Phyllic and argillic alterations are developed overmost of the area, surrounded by propylitic alteration. Phyl-lic alteration persists below the oxidation zone and potassicalteration is not seen at surface, possibly as a result of anintense phyllic overprint or surface related weathering(Geological survey of Iran, 1973a).

3. Methods

The Hyperion Level 1R dataset acquired on 26 July2004 was used in this research for mapping alteration min-erals. Since the data were acquired in summer the signal tonoise ratio was higher than winter datasets (King et al.,2003). Pre-processing was implemented on the data inorder to remove noise and acquire surface reectance.The spectra of diagnostic alteration minerals wereextracted from pre-processing imagery using ENVIs n-dimensional visualizer and a priori knowledge of the geol-ogy. In order to acquire information about spectral charac-teristics of alteration minerals, a number of representativesamples were collected and spectrally measured usingASD (Analytical Spectral Devices) FieldSpec Pro spec-trometer at the Mineralogy Laboratory of the Universidaddel Pas Vasco/Euskal Herriko Unibertsitatea (UPV/EHU). The extracted spectra were used for mineral map-ping through the MTMF algorithm. Field samples werestudied to determine their mineralogical characteristicsusing transmitted and reected light microscopy and asbulk sample by X-ray diraction (XRD) on a PANalyticalXPert Pro diractometer at the SGIker facilities (UPV/EHU). Spatial distribution of the minerals identied byHyperion data was then veried by in situ inspection andeld observation as well as examining their correspondenceto geological and alteration maps of the study area.

3.1. Preprocessing data

The Hyperion data suers from noise and had to be cor-rected for abnormal pixels, striping, and smile, prior toapplication. A full dataset has 256 columns per 6460 lineswith 7.5 km instruments swath. The data have 12-bitquantization and are stored as 16-bit signed integers.Although this sensor was designed with 242 bands, level1R data is delivered with 44 bands set to zero. There is also

M. Hosseinjani Zadeh et al. / Advancespectral overlap between VNIR and SWIR sensors makingtwo bands redundant. Therefore, 196 out of 242 bandswould be used as input for image processing and identica-tion of target materials. These bands cover a spectralregion from 426.82925.41 nm (bands 857) in VNIR to932.642395.5 nm (bands 79224) in SWIR. At the begin-ning of the preprocessing, uncalibrated image bands andoverlay bands were eliminated. Striping was apparent,especially in the rst 12 VNIR and many SWIR bands. Ade-stripe algorithm that reduced the stripe and maintainedthe integrity of spectral information was implementedusing ENVI software (Research Systems Inc, 2003; Ede,2004; Darmawan, 2006). The destriping algorithm used inENVI calculates the mean for each line and then normal-izes the line to this mean (Ede, 2004). The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLA-ASH) algorithm, available in ENVI software, was imple-mented to obtain surface reectance data. This algorithmwhich was developed by Spectral Sciences, Inc, under thesponsorship of the US Air Force Research Laboratory, isa MODTRAN4-based atmospheric correction softwarepackage (Adler Golden et al., 1999). It was designed toeliminate atmospheric eects through derivation of atmo-spheric properties such as surface albedo, surface altitude,water vapor column, aerosol, and cloud optical depths, aswell as surface and atmospheric temperatures from hyper-spectral data.

While de-striping removes noise partly and gives impres-sive results, some excessive noises including the abnormalpixels with negative digital numbers (DN) and pixels withconstant and intermediate DN values in an entire columnstill remain at a number of bands. Fortunately, these noisescan be identied visually and statistically. The remainingabnormal pixels and stripes were identied visually oneby one and some of the noise bands such as bands 190and 203 which had constant abnormal pixels at columns112 and 114, respectively, were eliminated. A number ofabnormal pixels such as those in column 7 at bands 200and 201 were also eliminated after extracting spatial sub-sets on the datasets. In addition, to get rid of errors result-ing from water vapor, the relevant absorption bands(bands 121130 and 165180, covering 1356.41447.2 and1800.291951.57 nm, respectively) were eliminated. A spa-tial subset corresponding to the study area was derivedfrom columns 10256 and lines 19922718 (246 726 pix-els). By excluding bands having no information, havingabnormal pixels, and falling in the water absorption range,a set of 165 bands were left for further analysis (Table 1).

Hyperion data also suer from smile which mainlyaects the bands in the VNIR region. The smile, alsoknown as frown curve, is a spectral distortion that is typ-ically found in push-broom sensors. It refers to an across-track wavelength shift from center wavelength, which isdue to the change of dispersion angle with eld position(Goodenough et al., 2003; Jiang et al., 2007; Dadonet al., 2010). The eect of smile is not obvious in individualbands. However, it becomes observable when the image is

Space Research 53 (2014) 440451 443transformed into Minimum Noise Fraction (MNF) space(Green et al., 1988). For Hyperion images with signicant

smile, there is a brightness gradient appearing in the rsteigenvalue image. There is no brightness gradient inMNF for images without signicant smile. The smile eectmay vary from one image to another. MNF algorithmimplemented on the data showed no brightness gradientin MNF eigenvalue image (Fig. 3).

Tominimizing uncorrelated spatial noisesMNFwas usedto segregate these noises (Green et al., 1988). The rst fewMNF bands usually convey the most useful information,

while subsequent bands increasingly have higher noise.MNF bands with calculated eigenvalues below one usuallydo not carry useful information and mainly contain noise(Jenson, 2005). In order to remove the noise and to get satis-fying results, MNF bands with eigenvalues more than 1.9were selected and the inverse MNF was carried out. As aresult, only a subset of 20 MNF-bands were maintainedand retransformed to reectance data. The resulting reec-tance dataset contained 165 bands, but onlywith the spectralinformation of the chosen MNF bands.

Finally, the ecacy of atmospheric correction and noiseremoval was assessed by comparison of green vegetationspectra extracted from the image with the same spectraobtained from a spectral library (Fig. 4). The overall shape,

pure pixels and to determine their spectra. These spectracan be derived from FLAASH-calibrated images based

Table 1List of the selected 165 bands used for this study.

Array Bands Wavelength (nm)

VNIR 857 426925SWIR 7993 9321073

9598 10941124100115 11441295117120 13161346131164 14571790181189 19612042191202 20622173204224 21932395

444 M. Hosseinjani Zadeh et al. / Advances in Space Research 53 (2014) 440451Fig. 3. First eigenvalue image of MNF transformation.on the spatial locations or can be taken from MNF imagesby inverting MNF plots to spectra. The extracted spectrawere used as reference for subsequent processing. Thesespectra determine a number of endmembers, some of whichincluding the characteristic NIR plateau between 700 and1300 nm, as well as absorption bands related to chlorophyll(498 and 680 nm) and leaf water (980,1190 nm) wereclearly evident in the reduced Hyperion data.

3.2. Endmember extraction

Information extraction from a Hyperion data setinvolves several processes including extraction of scenespectral endmembers using an integration of MNF, pixelpurity index (PPI), and n-dimensional visualizerapproaches (Boardman, 1993; Boardman et al., 1995;ENVI user guide 2003).

A pixel purity index was applied to the 20 MNF imageswith 10,000 projection of the scatter plot and a thresholdfactor of 2.5. Density slice thresholds were performed onresults to determine pixels with high digital numbers. Highvalues were used to compute the region of interest (ROI)for n-dimensional visualization. N-Dimensional visualiza-tion was applied to the ROI on MNF images to extractFig. 4. Spectrum of green vegetation extracted from pre-processedHyperion imagery, and the USGS spectral library.

could be attributed to alteration zones. The extracted spec-tra were characterized using spectral analysis procedureavailable at ENVI and visual inspection as well. Thesespectra were also compared to the laboratory spectra ofeld samples and existing reference library spectra suchas those at the USGS (Fig. 5).

3.3. Mineral mapping by MTMF

The extracted spectra were used to identify alterationminerals and to generate thematic mineral maps usingsub-pixel mixture tuned matched ltering (MTMF)method. Geometric correction was performed after imple-mentation of MTMF to keep original DN values of theimagery. MTMF is a partial sub-pixel method that com-bines the strength of the matched lter (MF) method withphysical constraints imposed by mixing theory in which thesignature at any given pixel is a linear combination of the

individual components contained in that pixel. It oers arapid means for detecting specic minerals based onmatches to specic library or image endmember spectra.Matched ltering removes the requirement of knowing allof the end-members by maximizing the response of aknown end-member and suppressing the response of thecomposite unknown background, thus matching theknown signature (Chen and Reed, 1987; Stocker et al.,1990; Research Systems Inc, 2003). The importance of mix-ture tuned matched ltering in identication of alterationminerals has already been revealed in many studies (Board-man et al., 1995; Kruse et al., 2003; Ellis and Scott, 2004;Bishop et al., 2011; Hosseinjani Zadeh, 2008; HosseinjaniZadeh and Tangestani, 2011; Hosseinjani Zadeh et al.,2013). Results of this algorithm are two sets of gray imagesfor each endmember including the matched lter (MF)image score and the infeasibility image. The MF imageshelp to estimate relative degree of match to the reference

spe

M. Hosseinjani Zadeh et al. / Advances in Space Research 53 (2014) 440451 445Fig. 5. Mineral spectra extracted from Hyperion comparing to convolved

Muscovite (Mu), (c) Illite (Il), (d) Kaolinite (Kao), (e) Goethite (Goe), (f) HemLib are abbreviations of Hyperion and Library, respectively. The red verticalctra from eld samples and reference library spectra. (a) Biotite (Bio), (b)

(Hem), (g) Jarosite (Ja), (h) pyrophylite (Pyr), (i) Chlorite (Ch). Hyp andlines indicate locations of diagnostic absorption features.

spectrum and the approximate sub-pixel abundance withvalues from zero to one. The infeasibility results are innoise sigma units and indicate the feasibility of the MFresults. This image is used to reduce the number of falsepositives that are sometimes found when using MF. Pixelswith a high infeasibility are likely to be MF false positives.Pixels which were mapped correctly had MF scores abovethe background distribution around zero and a low infea-sibility value. In order to get satisfactory results and selectpixels that matched well with the reference endmember, theMF score band and the infeasibility band were used to cre-ate a 2-dimensional scatter plot. To understanding mineralfractions at each pixel, pixels of low infeasibility and MFscore higher than approximately 0.25 were highlightedand divided into four groups including 0.250.35, 0.350.50, 0.500.75, and 0.751. These values indicate percent-ages of each mineral at the pixel. For instance, value of0.25 shows that 25% of pixel contains the selected mineral.Regions of interest (ROI) matched to these pixels weredened; each ROI was assigned a unique color and wasdraped over Hyperion grey imagery (Fig. 69).

4. Results and discussion

indicator of biotite (potassic-biotitic alteration zone)(Fig. 5(a)). This spectrum displayed an absorption featureat 2203 nm and small changes in the intensity of reectanceat VNIR, which were not shown in library spectrum of bio-tite. The 2203 nm absorption is due to AlOH vibration inminerals such as muscovite and illite (Fig. 5(b) and (c)).However, muscovite spectrum shows a deeper absorptionthan illite. Absorptions in 2163 nm (band 201) and2203 nm (band 205), due to Al-OH vibration, were indica-tors of kaolinite (Fig. 5(d)). The spectrum which wasassigned to goethite showed strong absorption at 487 nm,and a broad absorption feature at 932 nm, matching thebands 14 and 79, respectively (Fig. 5(e)). The spectrum withabsorptions at 487 nm and 884 nm (band 53) could beassigned to hematite (Fig. 5(f)). The spectrum whichshowed absorptions at 932 nm and 2264 nm (bands 79and 211, respectively) was jarosite (Fig. 5(g)). Commonly,absorption features of these minerals at VNIR region aredue to electronic transitions of ferric iron and the SWIRabsorption features of jarosite results from FeOH and OHstretches and bending. Hyperion spectra of goethite, hematiteand jarosite also showed absorption at 2203 nm due to mix-ture with AlOH bearing minerals. The spectra with diagnos-tic absorption features at 2163 nm (band 201) and 2335 nm(band 218), due to AlOH and MgOH, were indicators of

446 M. Hosseinjani Zadeh et al. / Advances in Space Research 53 (2014) 440451Endmember extraction yielded average spectral signa-tures for most of the alteration minerals including biotite,muscovite, illite, kaolinite, goethite, hematite, jarosite,pyrophyllite, and chlorite (Fig. 5). The spectrum with diag-nostic absorption features at 2254 and 2324 nm which coin-cided Hyperion bands 210 and 217, respectively, wasFig. 6. Mineral fraction map of biotite derived from MTMF algorithm. The laimages indicate Sarcheshmeh, Sereidun and Darrehzar PCDs.pyrophyllite and chlorite, respectively (Figs. 5(h) and (i)).Results of MTMF obtained a fraction map for each

mineral. Discriminated areas successfully correspond thealteration zones around Sarcheshmeh, Darrehzar, andSereidun (Fig. 69). Most pixels showed a mixture ofrge image indicates discriminated minerals at whole area and the zoomed

rati

s inalteration minerals so that more than one mineral was dis-criminated in a unique pixel. This was conrmed by eldstudies as a mixture of various minerals observed in aunique alteration zone. For example, small amounts of

Fig. 7. Mineral fraction map of biotite at Sarcheshmeh overlaid by its altezones derived from the alteration map of Sarcheshmeh.

M. Hosseinjani Zadeh et al. / Advancekaolinite or chlorite could be expected in phyllic zone.Exposures of biotite were restricted to Sarcheshmeh withvarious fractions (0.251.00), and in 4 pixels with low frac-tions (0.250.35 in two pixels; 0.350.50, and 0.500.75,each in one pixel), at northern Darrehzar (Figs. 6).

According to Sarcheshmeh alteration map (Fig. 2(a)) andeld studies, discriminated areas correspond the locations ofbiotitic and potassic alterations. Locations of potassic andbiotitic alterations were derived from Sarcheshmeh map(Fig. 2(a)), and were overlaid the discriminated biotite pixels(Fig. 7). Results showed that higher fractions (0.501.00)were discriminated within potassic alteration while lowerfractions (0.500.25) were matched to the biotitic alteration.Furthermore, pixels which were discriminated out of themap border coincided the mine tailings.

Sericite, illite, and kaolinite were discriminated at Sar-cheshmeh, Darrehzar, and Sereidun (Figs. 8(ac)). Sericiteis dominant at these areas while kaolinite and illite aredisseminated.

Goethite, hematite, and jarosite were also discriminatedat all three altered districts (Figs. 8(df)). However, the num-ber of pixels which were discriminated as goethite and hema-tite were more abundant than jarosite. Goethite andhematite were mostly mapped at east and south of theSarcheshmeh which coincide the mine tailings. Jarosite wasdiscriminated in a few pixels at the northeast and southwestof Sarcheshmeh. Low fractions of oxide minerals atSarcheshmeh could be attributed to the exploration activitieswhich have caused transferring the oxidized overburdensfrom this district. Goethite was distinguished extensively atDarrehzar with fractions 0.251.00, while hematite and jaro-

on map; black and green lines show extensions of the potassic and biotitic

Space Research 53 (2014) 440451 447site were less distributed. Jarosite was discriminated at thecenter and southwest, and hematite was identied at thenortheast of the area. Results obtained from Darrehzarshowed thatmost parts of this district consist of sericite, goe-thite, subordinate hematite, and jarosite, with disseminatedkaolinite and pyrophyllite. Pervasive distribution of ironoxides such as goethite and hematite at this area conrmsthe extensive oxidation at Darrehzar produced as a resultof supergene alteration. Goethite was discriminated at thecenter and west of Sereidun with fractions 0.250.50, whilejarosite was only discriminated at eight pixels with fractions0.250.35. Barzegar (2007) suggested that leached zone (as aproduct result of the supergene alteration) increases fromwestern to the central part of the prospect and decreasestoward the east, and limonite with various colors is associ-ated with abundant goethite throughout the Sereidun pros-pect. Discriminated areas conrmed this statement so thatgoethite is the dominant iron oxide at Sereidun and it wasdiscriminated at the center and west of the area. Discrimina-tion of pyrophyllite is limited to Sereidun and more scarcelyat Darrehzar and Sarcheshmeh (Fig. 8(g)). Laboratory spec-troscopy and XRD of eld samples conrmed the existenceof pyrophyllite at Sereidun. Chlorite, as indicator of propy-litic alteration was discriminated in fractions 0.251.00,chiey between Sarcheshmeh and Darrehzar, and aroundthree altered districts which surrounding other alterationminerals (Fig. 8(h)).

s in448 M. Hosseinjani Zadeh et al. / Advance5. Accuracy assessment

Accuracy of discriminated minerals was assessed bymineralogical analysis of collected samples, checking theveracity of identied minerals by comparison to corre-sponding eld samples and large-scale alteration maps ofSarcheshmeh and Darrehzar. The visual inspection of dis-criminated alteration minerals show good correlation withthe alteration maps (Figs. 2 and 9).

Field observation and sampling was carried out between24 and 27 November, 2011, at Sarcheshmeh, Sereidun andDarrehzar areas. Sixty-ve samples were systematically col-lected from fresh and weathered representative alteredrocks, and were localized by a global position system(GPS). Spectral characteristics and mineralogical proper-ties of samples were then analyzed by ASD instrument,optical microscopy, and XRD. The measured spectra by

Fig. 8. fraction map of alteration minerals derived from MTMF algorithmPyrophyllite, (h) Chlorite.Space Research 53 (2014) 440451ASD were interpreted and the abundance of each mineralin a spectrum was determined based on wavelength, inten-sity, and shape of the main absorption features. Relativeabundances of mineral phases in XRD results were madeby comparing the intensities of the main peaks and the pro-portions of them were estimated semi-quantitatively.

Checking the virtual Hyperion mineralogy of each pixelagainst the minerals obtained from ASD, XRD, andpetrography yielded an acceptable agreement. Comparisonof Hyperion spectra to the corresponding measured spectraof samples and reference library spectra such as USGS con-rmed that in most cases these spectra matched reasonably(Fig. 5). However, slight dierences were observed mostlydue to mixture and co-occurrence of alteration minerals.Biotite, chlorite, goethite, jarosite and hematite show mix-tures with sericite so that diagnostic absorption feature ofsericite (in 2203 nm) was also observed in their spectra.

. (a) Muscovite, (b) Illite, (c) Kaolinite, (d) Goethite, (f) Hematite, (g)

s inM. Hosseinjani Zadeh et al. / AdvanceIn addition, Hyperion spectra exhibited subtle features notevident in the laboratory spectra. Some dierences could bean eect of pixel size, causing greater mixing in Hyperiondata compared to the library spectra. This is due to the factthat eld and library spectra were acquired from smallerareas and purer samples, while the Hyperion spectra areindicators of an area of 30 30 m with commonlyunavoidable mixtures. Small amounts of a collected sam-ple, therefore, do not exactly represent the spectral proper-ties of the associated Hyperion pixel. However, the image-extracted spectra with obvious spectral signature could beindicative of dierent types of alteration minerals. Forinstance, the illite spectrum collected from eld samples(black curve in Fig. 5(c)) and Hyperion imagery (red curvein Fig. 5(c)) show similar deep absorption band at 2203 nm

Fig. 9. (a) Final classication image map of alteration minerals derived from MBio, Mu, Il, Kao, Goe, Hem, Ja, Pyr, and Ch indicate Biotite, Muscovite, Irespectively.Space Research 53 (2014) 440451 449as well as shoulder absorptions at 2143 nm and 2274 nm,which are also observed for ideal illite in a spectral library.

TMF algorithm. (b) Sarcheshmeh mine; (c) Sereidun, and (d) Darrehzar.)llite, Kaolinite, Goethite, Hematite, Jarosite, pyrophyllite, and Chlorite,

Table 2Counting percentages in which discriminated minerals by Hyperion datawere similar to the spectroscopic and laboratory results for each mineral.

Mineral ASD (%) XRD%

Biotite (52/70) 74 (52/70) 74Muscovite (47/70) 67 (33/70) 47Illite (48/70) 68 (46/70) 66Kaolinite (54/70) 77 (53/70) 76Goethite (62/70) 88 (61/70) 87Hematite (53/70) 76 (53/70) 76Jarosite (57/70) 81 (57/70) 81Pyrophyllite (59/70) 84 (60/70) 86Chlorite (56/70) 80 (48/70) 69

s inResults of XRD and thin sections microscopic analysesalso conrmed the occurrence of discriminated mineralsat the study areas, and showed that most samples consistedsecondary minerals formed by hydrothermal alteration andweathering processes.

In order to check the veracity of minerals identied byHyperion, results were compared to the ground samplesvia the ASD and XRD analyses. If the result of Hyperiondata was similar to ASD and XRD outputs, value of 1 wasadopted; otherwise, 0 was assigned to the sample location.Frequency was then applied to generalize the results and todetermine the number of measurements in which the resultof Hyperion data was similar to the led data. Percentagesof the correctly classied minerals were shown in Table 2.This assessment revealed that in most cases correctly clas-sied minerals were more than 75%. However, the excep-tion was for muscovite and illite which showed lowerpercentages, especially the lowest percentage obtained forXRD (47%) (Table 2). This could be attributed to the factthat in most cases it is dicult to discriminate illite andmuscovite from each other due to similarity of compositionand spectral characteristics. In addition, in XRD bulkanalysis muscovite may be confused with illite. Samplepreparation for XRD bulk analysis distorts preferentialorientation of phyllosilicates (especially illite and musco-vite). For an adequate evaluation of samples with abun-dant muscovite and illite more sophisticated treatmentsuch as oriented XRD analysis which requires more timeand cost would be applied. Since spectroscopic studiescould distinguish illite and muscovite more precisely thanXRD, the results was improved to 67%.

6. Conclusion

This study examined the feasibility of hyperspectralimagery for detection and mapping of alteration zonesaround porphyry copper deposits. It showed that thespaceborne Hyperion data can detect alteration zoneswhile the data were subjected to pre-processing and noisereduction. This sensor was eective for mapping a varietyof minerals characteristic of hydrothermally altered rocksincluding muscovite, illite, kaolinite, chlorite, pyrophyllite,biotite, hematite, jarosite, and goethite. Potassic-biotitic,phyllic, argillic, and propylitic alteration zones as well asgossans were distinguished and were discriminated fromsurrounding rocks. The ability to discriminate biotite,hematite, goethite and jarosite makes this instrument usefulfor mineral exploration, discrimination of strong pyritiza-tion, gossans, and mine tailings. In addition, such enhance-ments could be useful for identifying areas prone toenvironmental impacts by acid mine drainages, in whichminerals like jarosite, goethite, and hematite form undervarious acidic conditions. It is concluded that Hyperiondata provide signicant advantage over broad-band imagedata such as TM, ETM+ or ASTER in facilitating detailed

450 M. Hosseinjani Zadeh et al. / Advanceand comprehensive study of alteration minerals with mod-est need to eld and laboratory measurements.Acknowledgment

This work was funded by Research and DevelopmentCenter of National Iranian Copper Industries Company(NICICO). The authors are sincerely grateful to the geolo-gists and sta of the Sarcheshmeh copper mine for provid-ing the facilities and kindly helping us during our eldwork.

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Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data1 Introduction2 Geology and mineralization3 Methods3.1 Preprocessing data3.2 Endmember extraction3.3 Mineral mapping by MTMF

4 Results and discussion5 Accuracy assessment6 ConclusionAcknowledgmentReferences