Detecting Mapping Unknown Spectral Features from ALI, ASTER and Hyperion Images using Correlation Coefficient Method. Case Study: Sarcheshmeh copper mine, IRAN.


1 Mining Department, Shahid Bahonar University of Kerman, Kerman,.

2 Mining Eng. Department, Isfahan University of Technology, Isfahan, Iran

3 Mining Eng. Department, Shahid Bahonar University of Kerman, Kerman, Iran.

4 Department of Textile Engineering, Isfahan University of Technology, Isfahan, Iran.


One of the main objectives of employing multi and hyper spectral satellite imagery is to detect and identify the spectral behavior of different sacrificial coverage. To achieve this goal it is necessary to use algorithms capable of detecting such spectral differences both in lab scale and real field multi spectral images. In current study, we have developed an algorithm in MATLAB, based on converting mineral reflectance spectra into images, computing the correlation coefficient of the unknown spectra taken from satellite images (either multi or hyper spectral images) with those of published mineral reflectance spectra from USGS spectral library. The reflectance spectrum that is found with the highest correlation coefficient with the one that is extracted from an image is considered as the best match to the unknown spectrum. In order to evaluate the performance of this algorithm, we used the spectra of an indicator of alteration mineral from USGS spectral library. The results show that the algorithm could recognize the spectra from USGS spectral library with high degree of match. Furthermore, the performance of the algorithm was evaluated using real satellite datataken from phyllic alteration zone of Darrehzar porphyry copper deposit located 18 km south of Sarcheshmeh copper mine. Results show that the algorithm was capable of finding a match with the muscovite spectra from the USGS spectral library. Finally it is concluded that the spectra fitting based on correlation can effectively be used for mineral recognition using the already known spectra published by spectral research centers such as USGS.


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