Geological disasters, characterized by their destructive nature, pose significant threats to both human life and ecological environments. The advent of remote sensing technology has rendered hyperspectral remote sensing images an integral data source in monitoring and predicting these phenomena. However, it is noted that minor variations and detailed nuances within the images are often overlooked by traditional computer vision and deep learning techniques. Furthermore, data imbalances during the training of deep learning models have been identified as a potential hindrance to optimal performan...
