Selecting optimum base wavelet for extracting spectral alteration features associated with porphyry copper mineralization using hyperspectral images

Abstract

Extracting a set of meaningful spectral features could enhance the classification performance. This is particularly important in hyperspectral images where the dataset are very large and time consuming to process. Wavelet transform as a powerful decomposition tool in both low and high frequency components could play an essential role in extracting spectral features of target minerals. Selecting the optimum base wavelet is an important step in wavelet transform. In this research, two criteria to select optimum base wavelet were implemented on three wavelet series including Daubechie (db), symlet (sym) and coiflet (coif). Energy criterion involves entropy factor and energy-to-Shannon entropy ratio while matching shape criterion operates according to correlation coefficients. High ranking base wavelets in both energy and shape criteria, coif1, db3 and db7, are recommended to be utilized in hyperspectral image classification. Neural Network technique was used for classification and trained by means of mineral spectral features related to typical porphyry copper deposits. Non-Linear wavelet feature extraction was employed to select the efficient features as input data. The study area covered by Hyperion data contains two well-known porphyry copper deposits, Darrehzar and Sarcheshmeh, located in the Iranian copper belt. Based on classification error matrix, it is concluded that db7 through 12 selected features exhibits the maximum consistency with the field measured data and can be recommended as an appropriate base wavelet for detecting porphyry copper deposits.