ASTER satellite data processing for mineralization mapping was used to detect alteration and detect mineral exploration 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 atmosphere 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, datasets such as geology and structural maps, geochemistry, PIMA analyses (ground truthing), radiometrics, and

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 techniques 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 technique, and supervised spectral angle mapping (SAM) method seem to be equally applicable to all cases for detecting alteration zone and minerals. In this study, concentration-area, Crosta technique, and supervised spectral angle mapping method were used [6] [8] .

In recent years, application of remote sensing in mineral exploration had been developed and becoming an important 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 accelerate the exploration and reduce the cost [8] .

The principal component analysis (PCA), Crosta technique to know by person in 1901, in 1933 Helting suggested to calculate method [9] [10] . The target enter variable p of X₁ ... X_p, and know compound of p for component Z₁ ... Z_p, 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] .

The Studies area is located at 20 kilometers south-east of Dozdozan Town (Eastern Azarbaijan Province). According 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).

ASTER, ETM+ image processing techniques by PCA for band (1 ... 9 ASTE), band (1 ... 7 ETM+) for exist alteration 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] .

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).

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).

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 altration area. Therefore NW area is case study [36] - [43] . SAM image to show excess Philic at NW, E trend and capable 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 Charbonates 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).

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 geostatisic method used. XRD sample result show (Table 7) that result showed clay mineral and Iron oxide. The coordinates 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).

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 indicate 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 expands 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.

The paper was supported by Islamic Azad University, Science and research branch. The authors wish to acknowledge 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.