The studied area—Kermejegan—is located in the south of Qom Province, Iran. In this paper, geology map, ASTER and ETM7+ satellite images were used and after processing these images with Geomatica and ENVI softwares, iron potential zones were compared with the iron mine position in the south of the area and 2 dominate indexs around. Finally remote sensing, faults and geological data layers were integrated in GIS and hopeful zones were introduced for continuing the exploration processes.
The use of satellite images for mineral exploration has been very successful in pointing out the presence of minerals such as alunite, pyrophyllite, kaolinite, sericite, illite, muscovite, smectite, and carbonate which are important in the identification of hydrothermal alterations [
Kermejegan area is located between longitudes 481493 - 487103 and latitudes 3787390 - 3792893 in the south of Qom Province, Central Iran (
Principal Components Analisys (PCA) method was used for doing this phase.After processing with Geomathica software, RGB: (5 - 7) & PC2(5 - 7) & PC4(1,4,5,7) were used. The iron potential zones were shown in goldish yellow by this method (
The ASTER is an advanced optical sensor comprised of 14 spectral channels ranging from the visible to thermal infrared region. It will provide scientific and also practical data regarding various field related to the study of the earth [
Data layers have to integrate in GIS for getting any results.For this reason the geological layer with the location of fault on that should be the main layer and then the iron oxide potential zones which recognized by remote sensing integrated with that. In next step iron mines and indexes integreted with them and at last the final map prepared.
Least Squares Fitting (LS-Fit) is the technique assumes that the bands used as input values are behaving as the variables of a linear expression and the “y” value of the equation, namely the predicted band information, gives us a calculated output value. This predicted band is what that band should be according to the linear equation. The minerals which are sensitive to a specific band are then differentiated from the features which are reflective to the other bands as well; just by taking the difference between the predicted values and the original values [
Distribution of iron oxide was created by using all the 3 visible and near-infrared (VNIR) bands as the input bands and VNIR-b1 as the modeled band (
The Minimum Noise Fraction (MNF) transformation is used to determine the inherent dimensionality of image data, segregate noise in the data, and reduce the compu-
tational requirements for subsequent processing [14-16]. MNF involves two steps: in first step which is also called noise whitening, principal components for noise covariance matrix are calculated. This step decorrelates and rescales the noise in the data. In second step principal components are derived from the noise whitened data. The data can then be divided into two parts. One part associated with large Eigen values and the other part with near unity Eigen values and noise dominated images. Using data with large Eigen values separates the noise from the data, and improves spectral results [15,16]. MNF analysis can identify the locations of spectral signature anomalies. This process is of interest to exploration geologist because spectral anomalies are often indicative of alterations due to hydrothermal mineralization [
The Principal Component Analysis (PCA) is a multivariate statistical technique that selects uncorrelated linear combinations (eigenvector loadings) of variables. Each successively extracted linear combination, or principal component (PC), has a smaller variance. PCA is widely used for alteration mapping in metallogenic provinces [