The increasing demands for base metals such as copper and also the reduction of the superficial resources of these elements, have led to more advanced and sophisticated methods in exploratory studies of these mineral deposits. Remote sensing is considered as an accurate, rapid and low cost observatory system for mapping surface mineral alterations. Replacing the The objective of the current study is to provide a spectral pattern using intelligent techniques and hyperspectral images for exploring the porphyry copper mineralization. The study area covered by Hyperion data contains two well-known porphyry copper deposits, Darrehzar and Sarcheshmeh. After geometric and radiomeric corrections, wavelet transform was used to denoising Hyperion image with implementing hard and soft threshold filtering.To select optimum base wavelet, energy criterion and matching shape criterion were implemented on three wavelet series covering Daubechie (db), symlet (sym) and coiflet (coif). High ranking base wavelets in mentioned criteria, coif1, db3 and db7, were recommended to be utilized in hyperspectral image The performance of two feature extraction methods, including the Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) were compared.
