The study area is located in 58 km northeast of Isfahan in Central Iran (Figure 1(a)). Due to proximity to the desert, the climate of this area is warm temperature and dry in summer and cold temperature and dry in winter. The most important natural phenomena in the study area are hot spring systems, which have a significant impact on regional mineralization and creation of travertine and bentonite mines. Mainly the area consists of Devonian to Quaternary sedimentary units. Remote sensing instruments can provide detailed information on the mineralogy and geochemistry of the rock types comprising the Earth’s surface, and have been used for decades to map rocks, mineral assemblages and weathering characteristics [1] - [6] .

The type of land cover or mineral composition of the surface of the earth is determined using multispectral data based on the spectral effects. The Landsat and Terra satellites are of the most successful earth’s surface observation systems. Regarding huge advancements in sensors technology, the spectral and spatial resolution of remote sensing data has been improved. The VNIR spectral range is used by nearly all the broadband reflective multispectral sensor systems. The SWIR spectral range is regarded as the most effective for lithological and mineral mapping because most rock types have the most reflectance in the 1.55 - 1.75 µm range (Figure 2) and hydrous (clay) minerals (often products of hydrothermal alteration) have diagnostic absorption features in the spectral range 2 - 2.4 µm [7] . The advanced spaceborne thermal emission and reflection radiometer (ASTER), launched on board NASA’s Terra spacecraft in December 1999, were developed based on the success of airborne Thermal Infrared Multispectral Scanner (TIMS). ASTER consists of three separate subsystems with a total of 14 spectral bands across the VNIR, SWIR and TIR with three, six and five bands in each part of the spectrum respectively [8] . Several recent studies have focused on lithological mapping using ASTER data including all VNIR, SWIR and TIR wavelength regions [9] [10] [11] [12] [13] . ASTER sensor has a

good combination of spatial resolution in the VNIR, SWIR bands and multispectral thermal data bands which are very useful in geological applications (Figure 2).

The VNIR bands with a resolution of 15 m are adequate for distinguishing broad categories of land surface such as vegetation, water, soils, urban areas, superficial deposits and general rock outcrops while the six narrow SWIR bands of 30 m resolution have the potential to map major mineral assemblages (rock forming and alteration) and lithological unites [7] . Landsat 8 is the latest satellite in this series of Landsat satellites picking up images using two sensors. The first sensor is the operational land image (OLI), that takes the image from 9 short wavelength bands with a resolution of 30 meters for the VNIR and SWIR bands and the second sensor is a thermal infrared sensor (TIRS) that takes the image from two bands of thermal wavelength and includes a panchromatic band with a resolution of 15 meters [15] . In this study, we used the 1T Level of OLI sensor data with it’s the panchromatic band and also VNIR, SWIR bands of 1B level of ASTER sensor data. Geological maps on the scale of 1:25,000 are the most important tools for presenting geological surveying results and regionally display geological unit, especially their distribution, composition, structural relation and age. Collecting the existing geological data, preparing topographical maps for field surveys, providing necessary equipments for field work, informing the authorities, gathering samples and recording of field observations are required to prepare geological maps in field method. In field works, expenses such as cost of travel, vehicle, equipment and assistance are the most costly part of the program and providing and supplying of the items mentioned above is necessary for any of these field acquisitions to control the costs of the mapping project [16] . This research aimed to use satellite data in the preparation of geological map, which will lead to saving time and reducing coast, as well as increasing the accuracy of mapping operations. So, in this method, access to geological maps is more cost effective.

The study area is tectonically considered as sedimentary part of the Sanandaj-

Sirjan Zone. The Sanandaj-Sirjan Zone is located between the Urumieh-Dokhtar Magmatic belt and the Zagros Fold-Thrust Belt [17] . The Urumieh-Dokhtar Magmatic belt is composed of a series of massive volcanic and pyroclastic rocks associated with it that extends from Sahand to Bazman and have seen wide activities in Cenozoic. In the Eocene, the most intense volcanic activities have occurred in this zone so that pyroclastic rocks in the south in form of Urumieh?Dokhtar belt and parallel to Sanandaj-Sirjan belt have been developed and andesite and dacite rocks have covered a vast part of the area [18] . The Qom-Zefreh fault with the approximate NW-SE trend divided the Sanandaj-Sirjan Zone and The Urumieh-Dokhtar Magmatic belt. The eastern part of the fault Qom-Zefreh includes intrusive and volcanic rocks and its Western part includes high fractured sedimentary units that our study area is located in this section Figure 1(a). In economic terms, hot springs have formed the travertines of the region and these mines are currently active. In terms of stratigraphy, the most important rocks and sediments covering the area can be classified according to the age and the time of the appearance in the following order from old to new accordingly Figure 3.

The oldest rock units (Paleozoic period) of the study area are the Bahram and Jamal formations, which consists of fossiliferous limestone, dolomite and shale.

The Sorkh shale, Shotory and Naiband formations are formed in the Mesozoic period, which contains red ferruginous quartzite, shale, dolomite and grey limestone.

In the Jurassic and Triassic periods, sedimentary units such as shale, sandstone, red conglomerate, and limestone are formed in the region. The lower red formations (upper and lower members) and Qom formation are the most important formations in the Cenozoic period, which consists of conglomerate, red sandstone and yellow fossiliferous limestone. In the Quaternary period, the most important lithology in this area is travertine from hot springs [19] .

Geological formations were determined by examining the images obtained from

the Matched Filtering and Spectral Analogues methods and also, the exact boundary of each formation was drawn through comparing these images to the image obtained from the FCC with RGB (652) (Figure 1(b)).

Maps help us to measure the location and distribution of the resources, to analyze their interactions, to find suitable locations for specific actions and plan for future events. To obtain better results for the map data, the determination of their accuracy is mandatory.

For accuracy assessment of the formation’s map, the thematic accuracy method was done. The thematic accuracy deals with labels or descriptive properties of the map’s features and modules the difference between phenomena’s label and names on map and their actual nature on Earth.

The thematic accuracy done in two ways:

Non-Site-Specific Accuracy evaluation and the Site-Specific Accuracy evaluation which in former, only the total area of classes is calculated and their location is not considered. However, in the Sites specific accuracy evaluation, in addition to calculating the total area of classes, their location is also considered. In this project, the sites specific accuracy assessment method was used [29] .

After drawing, the classification map (Figure 7) and reference map (Figure 3) were examined using ENVI software and the Confusion Matrix method in terms of location and the area of the formations. Use Confusion Matrix to show the accuracy of a classification result by comparing a classification result with ground truth information [30] . The Confusion Matrix report shows the overall accuracy, kappa coefficient, errors of commission (percentage of extra pixels in class), errors of omission (percentage of pixels left out of class), producer accuracy, and user accuracy for each class [24] .

The kappa coefficient (K) “Equation (1)” is another measure of the accuracy of the classification. It is calculated by multiplying the total number of pixels in all the ground truth classes (N) by the sum of the confusion matrix diagonals ( ∑ i = 1 k n i i ), subtracting the sum of the ground truth pixels in a class times the

sum of the classified pixels in that class summed over all classes ( ∑ i = 1 k n i + × n + i ), and dividing by the total number of pixels squared minus the sum of the ground truth pixels in that class times the sum of the classified pixels in that class summed over all classes [24] .

K = N ∑ i = 1 k n i i − ∑ i = 1 k n i + × n + i N 2 − ∑ i = 1 k n i + × n + i (1)

The overall accuracy is calculated by summing the number of pixels classified correctly and dividing by the total number of pixels “Equation (2)”. The ground truth image or ground truth ROIs defines the true class of the pixels. The pixels classified correctly are found along the diagonal of the confusion matrix table which lists the number of pixels that were classified into the correct ground truth class. The total number of pixels is the sum of all the pixels in all the ground truth classes [24] .

O A = ∑ i = 1 k n i i N (2)

In this regard ( ∑ i = 1 k n i i ) is sum of diagonal elements and (N) the total number of pixels [31] .

The output error images are mask images, one for each class, where all correctly classified pixels have a value of 0 and incorrectly classified pixels have a value of 1. The last error image band (Figure 8) shows all the incorrectly classified pixels for all the classes combined [24] . Using the Kappa index value, the accuracy rate for the selected formations is 86%, so this map’s accuracy is acceptable (Table 3).

In this research, the ASTER and OLI data were used.. The noise data value reduced significantly by applying the atmospheric, geometric and topographic corrections. The SWIR and VNIR bands were used in the ASTER and OLI data for features detection. For a better detection of the region’s formations using the Region of Interest method the selected train was taken in both data, considering that each formation consisted of several sedimentary units and it was affected by recent sediments. These selected trains were used for OLI data and ASTER data in the

Spectral Analogues and the MF method, respectively. At last, by comparing the formations obtained from these two methods and the RGB (652) band composite in OLI data, the formation’s map of the region was drawn. In the Spectral Analogues method, the MF/SAM output images have the most adaptation to the region’s formations according to examining the SAM, MF, MF/SAM output images. Due to higher radiometric resolution, OLI data is more efficient than ASTER data in detecting the region’s formation. The thematic accuracy method was used for formation’s map accuracy assessment. In terms of area and location, each formation in the drawn map was compared with the reference map and finally, the map accuracy 86% was confirmed.

Vincheh, Z.H. and Arfania, R. (2017) Lithological Mapping from OLI and ASTER Multispectral Data Using Matched Filtering and Spectral Analogues Techniques in the Pasab-e-Bala Area, Central Iran. Open Journal of Geology, 7, 1494-1508. https://doi.org/10.4236/ojg.2017.710100

ASTER: Advanced Space borne Thermal Emission and Reflection Radiometer

ENVI: Environment for Visualizing Images

ETM+: Enhanced Thematic Mapper Plus

FCC: False Color Composite

FLAASH: Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes

µm: Micrometer

MF: Matched Filtering

MNF: Minimum Noise Fraction

MTMF: Mixture Tuned Matched Filtering

NASA: National Aeronautics and Space Administration. www.gsfc.nasa.gov

OLI: Operational Land Imager

RGB: Red Green Blue

ROI: Region of Interest

SAM: Spectral Angle Mapper

SNR: signal-to-noise ratio

SPOT: System Pour Observation de La Terre

SWIR: Short Wavelength Infrared

TIMS: Thermal Infrared Multispectral Scanner

TIR: Thermal Infrared

TIRS: Thermal Infrared Sensor

USGS: United States Geological Survey. www.usgs.gov

VNIR: Visible and Near Infrared