Application of Different Image Processing Techniques on Aster and ETM+ Images for Exploration of Hydrothermal Alteration Associated with Copper Mineralizations Mapping Kehdolan Area (Eastern Azarbaijan Province-Iran)

Abstract

The Kehdolan area is located at 20 kilometers to the south-east of Dozdozan Town (Eastern Azarbaijan Province).
According to structural geology, volconic rocks are situated in
Alborz-Azarbyjan zone, and faults are observed in the same direction to this system with SE-NW trend. The results
show that kaolinite alteration trend with Argilic and propylitic veins is the same direction with SW-NE faults in this area. Therefore,
these faults with these trends can be considered as the mineralization control
for determination of the alterations. Different image processing techniques, such as false color composite (FCC), band ratios, color
ratio composite (CRC), principal component analysis (PCA), Crosta technique, supervised spectral angle mapping (SAM), are used for identification of the
alteration zones associated with copper mineralization. In this project ASTER data are process and spectral analysis to fit for recognizing intensity
and kind of argillic, propylitic, philic, and ETM+ data which are process and to fit for iron oxide and relation to metal
mineralization of the area. For recognizing different alterations of the study
area, some chemical and mineralogical analysis data from the samples showed
that ASTER data and ETM+ data were capable of hydrothermal
alteration mapping with copper mineralization. Copper mineralization in the region is in agreement with argillic
alteration. SW-NE trending faults controlled the mineralization process.

Full text

Open Journal of Geology, 2014, 4, 582-597
Published Online November 2014 in SciRes. http://www.scirp.org/journal/ojg
http://dx.doi.org/10.4236/ojg.2014.411043
How to cite this paper: Hajibapir, G., et al. (2014) Application of Different Image Processing Techniques on Aster and ETM+
Images for Exploration of Hydrothermal Alteration Associated with Copper Mineralizations Mapping Kehdolan Area (East-
ern Azarbaijan Province-Iran). Open Journal of Geology, 4, 582-597. http://dx.doi.org/10.4236/ojg.2014.411043
Application of Different Image Processing
Techniques on Aster and ETM+ Images for
Exploration of Hydrothermal Alteration
Associated with Copper Mineralizations
Mapping Kehdolan Area (Eastern
Azarbaijan Province-Iran)
Golchin Hajibapir1, Mohammad Lotfi2, Afshar Zia Zarifi3, Nima Nezafati1
1Department of Geology, Islamic Azad University, Science and Research Branch, Tehran, Iran
2Department of Geology, Islamic Azad University, North Tehran Branch, Tehran, Iran
3Mining Engineering Department, Islamic Azad University, Lahijan Branch, Lahijan, Iran
Email: g.hajibapiir@gmail.com
Received 23 September 2014; revised 20 October 2014; accepted 13 November 2014
Copyright © 2014 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Abstract
The Kehdolan area is located at 20 kilometers to the south-east of Dozdozan Town (Eastern Azar-
baijan Province). According to structural geology, volconic rocks are situated in Alborz-Azarbyjan
zone, and faults are observed in the same direction to this system with SE-NW trend. The results
show that kaolinite alteration trend with Argilic and propylitic veins is the same direction with
SW-NE faults in this area. Therefore, these faults with these trends can be considered as the mine-
ralization control for determination of the alterations. Different image processing techniques,
such as false color composite (FCC), band ratios, color ratio composite (CRC), principal compo-
nent analysis (PCA), Crosta technique, supervised spectral angle mapping (SAM), are used for
identification of the alteration zones associated with copper mineralization. In this project
ASTER data are process and spectral analysis to fit for recognizing intensity and kind of argillic,
propylitic, philic, and ETM+ data which are process and to fit for iron oxide and relation to metal
mineralization of the area. For recognizing different alterations of the study area, some chemical
and mineralogical analysis data from the samples showed that ASTER data and ETM+ data were
capable of hydrothermal alteration mapping with copper mineralization. Copper mineralization in
the region is in agreement with argillic alteration. SW-NE trending faults controlled the minerali-

G. Hajibapir et al.
583
zation process.
Keywords
Kehdolan Area, False Color Composite, Band Ratios, Color Ratio Composite, Principal Component
Analysis, Crosta Technique, Supervised Spectral Angle Mapping, ASTER Data, ETM+ Data,
Alteration
1. Introduction
ASTER satellite data processing for mineralization mapping was used to detect alteration and detect mineral ex-
ploration targets [1] [2]. ASTER is the Advanced Spaceborne Thermal Emission and Reflection Radiometer, a
multi-spectral sensor onboard one of NASA’s Earth Observing System satellites, Terra, which was launched in
1999. ASTER sensors measure reflected and emitted electromagnetic radiation from earth’s surface and atmos-
phere in 14 channels (or bands). There are three groups of channels: three recording visible and near infrared
radiation (VNIR) at a spatial resolution of 15 m; six recording portions of shortwave infrared radiation (SWIR)
at a spatial resolution of 30 m; and five recording thermal infrared radiation (TIR) at a resolution of 90 m. The
higher spectral resolution of ASTER (compared to Landsat, for example—Figure 1) especially in the shortwave
infrared region of the electromagnetic spectrum makes it possible to identify minerals and mineral groups such
as clays, carbonates, silica, iron-oxides and other silicates. An additional backward-looking band in the VNIR
makes it possible to construct digital elevation models from bands 3 and 3b. ASTER swath width is 60 km (each
scene is 60 × 60 km) which makes it useful for regional mapping [3].
There are a few things to note when using ASTER imagery for regional mineralogical mapping. Firstly, cloud
cover, vegetation and atmospheric effects can severely mask or alter surface signals in this project ASTER,
ETM+ data to correct with log residuals calibration method at ENVI 5/1 software [4] [5]. Secondly, bands and
band ratios do not indicate the occurrence of a mineral with absolute certainty or with any idea of quantity, so
this step is essential on ground truth and set appropriate thresholds. Thirdly, every terrain is different, so ratios
which work in some areas for a particular mineral or assemblage may not show the same thing elsewhere. As a
result of these factors, it is important not to look at ASTER images in isolation from other data. If possible, da-
tasets such as geology and structural maps, geochemistry, PIMA analyses (ground truthing), radiometrics, and
Figure 1. Distribution of ASTER and Landsat channels with respect to the elec-
tromagnetic spectrum.

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584
any other available data should be used in conjunction with ASTER for best results [6] [7]. Several methods
have been conducted for recognizing different alterations with ASTER data. Different image processing tech-
niques such as false color composite, band ratios, color ratio composite, principal component analysis, Crosta
technique, supervised spectral angle mapping, and neural network classification are used for identification of the
alteration zones associated with copper mineralization. The principal component analysis (PCA), Crosta tech-
nique, and supervised spectral angle mapping (SAM) method seem to be equally applicable to all cases for de-
tecting alteration zone and minerals. In this study, concentration-area, Crosta technique, and supervised spectral
angle mapping method were used [6] [8].
2. Concentration-Area
In recent years, application of remote sensing in mineral exploration had been developed and becoming an im-
portant tool. Most important capability of satellites in mining exploration is recognizing altered area. Because of
close spatial relationship between mineral deposits and alteration, mineral mapping based of satellite data acce-
lerate the exploration and reduce the cost [8].
The principal component analysis (PCA), Crosta technique to know by person in 1901, in 1933 Helting sug-
gested to calculate method [9] [10]. The target enter variable p of X1 ... Xp, and know compound of p for com-
ponent Z1 ... Zp, do not correlation [9] [11]-[15]. Classification supervised spectral angle mapping (SAM) need
to ROI educationa file. SAM method by reason angel pixel to fabricate in N dimention with coordinates axise
[16] [17].
3. Geological Setting of the Case Studies
The Studies area is located at 20 kilometers south-east of Dozdozan Town (Eastern Azarbaijan Province). Ac-
cording to structural geology, volconic rocks are situated in Alborz-Azarbyjan zone, and faults observe in same
direction to this system with SE-NW trend that these are cut off with new faults with SW-NE trend. The results
show that kaolinite alteration trend with Argilic and propylitic veins are same direction with SW-NE faults in
this area.
Therefore, these faults with these trends can be considered as the mineralization control for determination of
the alterations. Oldest rock types in the area are Eocene Andesit-Basalt.
There are Eocene-Oligocene sedimentary units including: marl, Nummolitic sandy limestone, Tuff breccia.
There are Oligocene Syenit dyke as in central parts of the study. Geology map digiting for Arc Gis 10 soft
waer (Figure 2). The result ETM+ image processing techniques by False color composite (FCC (band 7, 4, 2))
show: pink color (volcanic rock), red brown color (iron oxide-manganese oxide), gray blue, white color (clay
minerals) was capable with Geological unit and Fult at geological map [18] [19]. FCC (band 7, 4, 2) image
processing for ENVI 5/1 software (Figure 3).
4. The Principal Component Analysis (PCA)
ASTER, ETM+ image processing techniques by PCA for band (1 ... 9 ASTE), band (1 ... 7 ETM+) for exist al-
teration for ENVI 5/1 software was calculated. Light point exist ASTER PC2 Image to show Altration area with
band math 4/9 (Figure 4). Light point exist ASTER PC7 reverse image to show vegetation cover with band
math 3/2 (Figure 5). Light point exist ETM+ PC5 image show Argilic Altration area with band math 5/7
(Figure 6). Light point exist ETM+ PC7 reverse image show iron oxide with band math 3/1 (Figure 7). Special
vector matrise PCA ASTER, ETM+ (Table 1, Table 2) [20]-[23].
5. Crosta Technique for Special Mineral Evidend
Crosta technique for muscovite mineral at band (1, 6, 7, 9 ASTER image) was calculated (Table 3). Moscovite
mineral have high reflection at band 7 and high absorption at band 6 [24]-[28].
High light exist at PC3 reverse image capable muscovite mineal, target of philic altration (Figure 8).
Crosta technique for kaolinite mineral at band (5, 6, 7 ASTER image) was calculated (Table 4). Kaolinite
mineral have high reflection at band 7, 5 and high absorption at band 6 [26] [29] [30].
High light exist at PC1 image capable kaolinite mineal, target of Argilic altration (Figure 9).

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Figure 2. Kehdolan geological map.
Figure 3. FCC (band 7, 4, 2) image (evident geological unit).

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Figure 4. Altration image.
Figure 5. Vegetation cover image.

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Figure 6. Argilic altration image.
Figure 7. Iron oxide image.

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Table 1. Special vector matrise PCA ETM+.
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Band 1 0.399579 0.200875 0.493121 0.154518 0.488643 0.383517 0.383517
Band 2 0.371749 0.438192 −0.225847 0.587692 −0.518341 0.048603 0.048603
Band 3 0.237005 0.144606 0.495941 0.119490 0.104559 −0.570855 −0.570855
Band 4 0.386382 0.500228 −0.176357 −0.750152 −0.077181 −0.018619 −0.018619
Band 5 0.000011 0.224469 −0.612875 0.230287 0.689673 −0.150528 −0.150528
Band 6 −0.704753 0.667916 0.230542 0.026524 −0.003510 0.040896 0.040896
Band 7 0.000000 0.000000 0.000000 0.000000 −0.000000 −0.707107 0.707107
Table 2. Special vector matrise PCA ASTER.
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9
Band 1 −0.984200 −0.062615 −0.063883 −0.062453 −0.062168 −0.062412 −0.062469 −0.062665 −0.062121
Band 2 −0.176090 0.427395 0.372248 0.367633 0.328495 0.329038 0.318682 0.305686 0.318474
Band 3 0.008835 0.163848 0.858800 −0.213433 −0.168468 −0.195883 −0.202049 −0.225476 −0.177678
Band 4 0.015454 0.859775 −0.337544 0.066219 −0.128494 −0.143264 −0.159675 −0.223912 −0.171930
Band 5 −0.004572 0.202582 −0.073348 −0.863289 0.127182 0.092710 0.006612 0.208492 0.374177
Band 6 −0.002133 0.077985 0.013448 −0.128062 −0.249657 −0.084614 0.342275 0.655681 −0.600646
Band 7 0.000521 −0.014035 −0.015863 −0.185674 0.693815 0.259831 0.117439 −0.306588 −0.555335
Band 8 0.000742 −0.000442 0.007915 0.015152 −0.230679 0.752930 −0.571919 0.168557 −0.155203
Band 9 0.000018 0.005981 −0.001192 −0.120972 −0.479831 0.422817 0.608391 −0.453716 0.017819
Table 3. Special vector matrise band (1, 6, 7, 9 ASTER image).
PC1 PC2 PC3 PC4
Band 1 0.994043 0.063001 0.063059 0.062708
Band 2 −0.108979 0.583396 0.568724 0.569496
Band 3 −0.000019 −0.216552 −0.570891 0.791952
Band 4 −0.001215 −0.780247 0.588778 0.211078
Crosta technique for carbonates (Cholorit, Epidotes, Calcite) mineral at band (1, 7, 8, 9 ASTER image) was
calculated (Table 5). This mineral have high reflection at band 7, 9 and high absorption at band 8 [26] [29] [31].
High light exist at PC4 reverse image capable Carbonates mineal, target of prophilitic altration (Figure 10).
6. Supervised Spectral Angle Mapping (SAM)
The result ASTER, image processing techniques by supervised spectral angle mapping (SAM) method with 0/1
angle do spectral on CRC ((B5 + B7)/B6, (B4 + B6)/B5, (B7 + B9)/B8) ASTER image (Figure 11) [32]-[35].
The result target to (Philic, Argilic, Prophilic) Altration exist at study area. The result target four important altra-
tion area. Therefore NW area is case study [36]-[43]. SAM image to show excess Philic at NW, E trend and ca-
pable Foid rich Syeinogabro unit geology (Figure 12). SAM image to show excess Argilic at central by SW-NE
trend and capable major fault area, Altered Syeinit unit geology (Figure 13). SAM image to show excess Char-
bonates at NW, SW, E, E trend and capable Andesit-basalt, tuff with limeston study area unit geology (Figure
14). The CRC (B7/B5, B2/B1, B3/B1) image (ETM+) to show red color excess iron oxid (Magnetit) (Figure
15).

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Figure 8. Moscovite mineral image.
Table 4. Special vector matrise band (5, 6, 7 ASTER image).
PC1 PC2 PC3
Band 1 −0.568074 −0.578693 −0.585154
Band 2 −0.437825 −0.389543 0.810287
Band 3 −0.696851 0.716498 −0.032077
7. Control with Geological Particulars, XRD, ICP, Doubly-Polished Thin Section,
Heavy Mineral
Altration the study area controlled by ICP sample and result obtained exist high copper and Fe element (Table 6)
three distribution for copper exist the probable anomaly and possible anomaly and field, distribution by geosta-
tisic method used. XRD sample result show (Table 7) that result showed clay mineral and Iron oxide. The coor-
dinates of sampling points in Kehdolan area show at Figure 16. Heavy mineral sample result showed (Table 8)
that native copper and iron oxide minerals.
Doubly-polished thin section result show at Figure 17, and result copper-iron mineralization. At all doubly-
polished thin section exist minerals: covellite and chalcosite (B, A-image), altered magnetite (C-image), pyrite
with Ti exsolution (E-image), bornite (D-image), altered magnetite with ilmenite exsolution (F-image), hematite
and chalcosite (G-image), covellite and pyrite (H-image).

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Figure 9. Kaolinit mineral image.
Table 5. Special vector matrise band (1, 7, 8, 9 ASTER
image).
PC1 PC2 PC3 PC4
Band 1 0.994027 0.063058 0.063258 0.062707
Band 2 −0.109131 0.575296 0.571368 0.575033
Band 3 −0.000545 0.362833 0.453723 −0.813933
Band 4 −0.000410 −0.730350 0.680935 0.054011
Figure 10. Charbonates mineral image.

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Figure 11. Final map (SAM).
Figure 12. Philic map (SAM).

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Figure 13. Argilic map (SAM).
Figure 14. Cholorit map (SAM).

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Figure 15. ETM+ iron oxide (Magnetite) image.
Table 6. ICP sample of Kehdolan area for Cu-Fe mineral.
Element X + s = field X + 2s = threshold X + 3s = probably anomaly
Cu 2367.29 4640.08 6912.87
Fe
41438.5
51844.5
62250.5
Sample name CU (PPM) Fe (PPM)
Kh-101
7699.63
Probable anomaly
38807
Field
Kh-102 133 Low field 23615 Low field
Kh-103
59
Low field
5516
Low field
Kh-106
5501
Threshold
4641
Low field
Kh-201 118 Low field 31320 Field
Kh-202
72
Low field
33415
Field
Kh-2-76 104 Low field 26200 Low field
Kh-2-78
7699.63
Probable anomaly
19500
Low field
Kh-2-96
5342
Threshold
32200
Low field
Kh-2-98 2247 Field 25800 Low field
Kh-2-99
352
Low field
31600
Field
Table 7. XRD sample of Kehdolan area.
Sample Major phase Minor phase Trace phase
KH-XRD-01 Sanidine Kaolinite
Ankerite Gypsum
Quartz Hematite
Montmorillonite
KH-XRD-02 Quartz Ankerite
Kaolinite
Sanidine
Hematite
Calcite
KH-XRD-03 Sanidine
Quartz
Kaolinite

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Figure 16. The coordinates of sampling points.
Table 8. Heavy mineral sample of Kehdolan area.
Field No. (ppm) K-H-M-1 K-H-M-2 K-H-M-3 K-H-M-4 K-H-M-5
Magnetite 1657/60 1132/20 1547/09 1491/84 3374/40
Hematite 589/12 360/69 1104/60 820/56 1570/49
Epidotes 588/00 960/00 735/00 136/50 870/83
Biotite 3/36 0/00 0/00 0/00 0/00
Pyrite oxide 28/00 34/29 350/00 19/50 248/81
Limonite 196/00 24/00 245/00 13/65 174/17
Martite 291/20 35/66 728/00 608/40 1035/05
Pyrite 20/00 20/00 0/21 0/47 0/03
Chalcopyrite 0.01 (4) 0.01 (4) 0/00 0/00 0.01 (2)
Native copper 0.01 (1) 0.01 (1) 0/00 0/00 0.01 (1)
Carbonates 10/84 0/12 12/65 0/08 24/52
Altered minerals 912/00 1144/29 854/00 567/00 232/07
Light minerals 0/00 0/09 9/33 0/06 18/10
(Namber mineral)

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Figure 17. Doubly-polished thin section of Kehdolan area.
8. Conclusion
Investigation shows that ETM+ data due to its blue region spectral band can enhance the iron oxide rich areas
much better than ASTER data. ASTER data due to its various spectral bands in the short wave infrared are more
capable of enhancing clay bearing areas. The results showed that Crosta technique, supervised spectral angle
mapping better method for enhancing alteration at ASTER data. Results obtained by study on Kehdolan area in-
dicate the potential use of the ASTER data to fit for kind alteration of argillic, propylitic, philic, and ETM+ data
are to fit for the iron oxide and relation to metal mineralization of the area. The study area Argilic alteration ex-
pands that relationship with mineralization copper which is the same direction with SW-NE faults in this area
and relationship with Oligocene Syenit dyke unit as in central parts of the study. Result study for control with
geological particulars, showed more probable anomaly distribution copper mineralization.
Acknowledgements
The paper was supported by Islamic Azad University, Science and research branch. The authors wish to ac-
knowledge the research deputy of Azad University, Science and Research Branch for supporting of this study.
The authors would like to thank the editors and reviewers of this paper for their comments and valuable remarks.
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http://dx.doi.org/10.1016/j.rse.2006.05.014