Evaluating the performance of the wavelet transform in extracting spectral alteration features from hyperspectral images

With the large number of spectral bands in hyperspectral images, the conventional classification methods commonly used for multispectral images are not effectively applicable. To overcome such difficulty, feature extraction methods could be used to reduce the dimension of hyperspectral images. In this study, the performance of the principal component analysis (PCA) as a widely used technique in feature extraction and the wavelet transform as a powerful decomposition tool on hyperspectral data is compared. In wavelet transform, a non-linear wavelet feature extraction was employed to select efficient features for more classification accuracy. Shortwave infrared bands of Hyperion imagery were selected as input data. The study area includes two well-known porphyry copper deposits, Darrehzar and Sarcheshmeh, located in the Iranian copper belt. Neural networks (NN), Support Vector Machine (SVM), and Spectral Angle Mapper (SAM) were used for multi-class classification based on hydrothermal alteration zones and then trained by mineral spectral features related to typical porphyry copper deposits. In the NN set-up used in this study, one hidden layer was used, with the number of neurons equal to the number of features in the input layer. Conjugate gradient backpropagation was employed as the network training function. Then, the efficiency of feature extraction methods was compared through their classification accuracies. According to the results, although the highest classification accuracy for the PCA method occurs in lower numbers of extracted features compared to wavelet transform, the wavelet transform outperforms the PCA, based on confusion matrix classification. Moreover, NN is stronger than SVM and SAM in discriminating favourable alteration zones associated with porphyry copper mineralization using hyperspectral images.