پديد آورندگان :
كاظمي محمد نويسنده دانشگاه علوم پزشكي شهيد بهشتي,; Kazemi M , احمدي سيدجواد نويسنده دانشيار پژوهشكده چرخه سوخت، سازمان انرژي اتمي , غلامي حميد نويسنده دانشگاه اراك,ايران Gholami Hamid , مهدوي رسول نويسنده
كليدواژه :
دگرساني , تنگ بستانك , سنجش‌ازدور , برازش مشخصه طيفي , پيكسل مبنا
چكيده لاتين :
Introduction
advanced remote sensing has been used in the past few decades in geology, mineral and hydrocarbon exploration. In the initial stages of remote-sensing technology development in the 1970s, geological mapping and mineral exploration were the commonest applications. Both multispectral and hyperspectral datasets can be used for mapping the alteration zones. alteration zones are commonly associated with certain minerals, such as propylitic assemblage (chlorite, epidote, and calcite), argillic minerals (kaolinite, dickite, montmo- rillonite), phyllic alteration minerals (sericite, illite), and advanced argillic minerals (alunite, pyrophyllite). Many studies reported the importance of remote sensing for mapping alteration minerals with ASTER data through image processing techniques, such as band rationing, principal component analysis (PCA), linear spectral unmixing (LSU), matched filtering (MF), mixture tuned matched filtering (MTMF), and constrained energy minimization (CEM). Most of these studies determined hydrothermally altered minerals at regional scale through per pixel analysis with little attention to subpixel analyses. However, an image pixel is often a mixture of the energy reflected or emitted from different materials that cannot be detected by per pixel classification algorithms. Rare publications are available for mapping alteration minerals using subpixel algorithms. Landsat 8 data can detect the altered rocks and ferrous minerals throw the OLI (Operational Land Imager) part of the image due to the absorption and reflectance characteristics of these rocks which appear in this range.
Methods
The Spectral Angle Mapper (SAM) algorithm is based on an ideal assumption that a single pixel of remote sensing images represents one certain ground cover material, and can be uniquely assigned to only one ground cover class. The SAM algorithm is a simply based on the measurement of the spectral similarity between two spectra. The spectral similarity can be obtained by considering each spectrum as a vector in q -dimensional space, where q is the number of bands. The SAM algorithm determines the spectral similarity between two spectra by calculating the angle between the two spectra, treating them as vectors in a space with dimensionality equal to the number of bands. (SFF) method. This is one of the algorithms nowadays used for satellite spectral analysis. Here, the similarity and conformity between the unknown image spectrum and the reference spectra are studied by investigating the reference spectra of the known signature and the recorded spectrum for each pixel in the satellite image. MTMF is a partial subpixel unmixing hybrid method based on the combination of well-known signal processing methodologies and linear mixture theory. This method combines the strength of the matched filter (MF) method (no requirement to know all the end members) with physical constraints imposed by mixing theory (the signature at any given pixel is a linear combination of the individual components contained in that pixel). The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a transformed coordinate space. The space is transformed according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. Also we used RMSE to evaluation these method with actual dolomite zones. root mean square error (RMSE) analysis was performed for 50 alteration-mapped pixel points derived from the image processing results and compared with real points on the ground obtained in the global positioning system survey(where Preal is realpointsonthegroundand Pestimated is alteration-mapped pixelpointsatpoint i). Also we listed errors of commission, omission, Kappa coefficient and overall accuracies. Errors of commission result when we incorrectly identify pixels associated with a class as other classes, or when we improperly separate a single class into two or more classes. Errors of omission occur whenever we simply don’t recognize pixels that we should have identified as belonging to a particular.
We studied the applicability of data from the recently launched Landsat-8 for mapping alteration areas and litho- logical units associated with SAM, MTMF, ACE, SFF, PCA and BR to identification alteration zones in the region in Fars provinceʹs Beheshte Gomshodeh.
Result and discussion
In Landsat8 Band 2 is positioned in the blue(0.450–0.515 _m), band 3 in the green(0.525–0.600 _m) and band4 in the red (0.630–0.680 _m) region of the electromagnetic spectrum. The natural RGB colour combination image was assigned to bands4, 3and2 for a full view of the image. Geological features and the geomorphological framework can be distinguished at regional scale. Using confusion matrix showed that among the various methods SFF least error and ACE has the maximum error. The SFF method is based on the comparison of absorption features in the image and the reference spectra. The distribution map of the indicator clay minerals, such as kaolinite, muscovite, illite , montmorillonite, alunite, pyrophyllite, dickite, chlorite, and epidote in Beheshte Gomshodeh exploratory area has been prepared with the help of this method. SFF method. This is one of the algorithms nowadays used for satellite spectral analysis. Here, the similarity and conformity between the unknown image spectrum and the reference spectra are studied by investigating the reference spectra of the known signature and the recorded spectrum for each pixel in the satellite image. We have used the SFF algorithm (for the processing of satellite images in this study) because it gives users the best results, compared with all other spectral analysis methods (in the ENVI software) used for satellite image processing Another advantage of SFF method (compared with other classification methods and spectral analysis algorithms) is that it has sensitivity to recording precise and subtle mineral absorption features in the spectral diagram of the mineral under consideration. In other words, in this method, even the smallest and the most suibtle absorption features are highlighted for the purpose of a thorough and precise study.
