Deep learning-based hydrothermal alteration mapping using GaoFen-5 hyperspectral data in the Duolong Ore District, Western Tibet, China

Hydrothermal alteration mapping is of great significance for porphyry copper deposit (PCD) exploration. Due to different metallogenetic environments, hydrothermal processes are heterogeneous especially in large-scale ore districts, leading to diverse physicochemical properties and spectral variations of wall-rock alteration. Conventional mapping methods mainly rely on shallow spectral features to identify hydrothermal alteration and cannot fully characterize the properties of the materials. By contrast, deep learning (DL) frameworks with multiple nonlinear layers can extract deep spectral and spatial features from hyperspectral imagery which is more robust and discriminative. Deep models were employed to the GaoFen-5 (GF-5) hyperspectral dataset in the Duolong district, to investigate the role of deep features in hydrothermal alteration mapping. For this purpose, we assess stacked autoencoder and several convolution neural networks which are computationally affordable for achieving large-area mineral exploration. More importantly, the mixed convolutions and covariance pooling algorithm achieved the best mapping results with the overall accuracy of 96.77%. The feature fusion method is also recommended, because of its lightweight structures. These two spectral–spatial classifiers produced appealing classification performance while reducing the misclassification among spectrally similar minerals and alleviating noise, demonstrating their powerful learning capabilities. Finally, spatial patterns of the hydrothermal alteration in the Duolong district can be taken as an important indicator of erosion degree of the porphyry-epithermal system. It is proved that the combination of the DL methods and the GF-5 hyperspectral data is effective in large-area mineral exploration and can be applied to other PCDs in the neighboring Bangong Co-Nujiang metallogenic belt.