Shadow-aware terrain classification: advancing hyperspectral image sensing through generative adversarial networks and correlated sample synthesis

In recent years, the utilization of hyperspectral sensors for remote sensing has marked a profound advancement due to the success of machine learning techniques. Nevertheless, difficulties still exist, especially in locations with shadows. The heterogeneity in spectral data due to different shadow origins, such as different types of clouds and different building designs, poses a significant obstacle to the advancement of shadow-aware classification algorithms. Furthermore, precisely labeling the underlying structures in shadowed areas is a very cumbersome effort. We present a loss function-based strategy based on generative adversarial networks to address this problem. Using the context of correlated samples, our loss function combines unpaired matchings and transitive style modifications via the fusion of contrastive learning, dual learning, cycle consistency, and curriculum learning algorithms. Our work transforms the non-shadowed training instances into the shadowed counterparts for use as synthetic training samples, as opposed to the conventional method of correcting shadowed pixels to their non-shadowed counterparts. We propose learning this transformation model with unpaired data samples, which is particularly advantageous compared with the collection process of the same samples with and without shadow. Synthetic samples for shadow-obscured regions can be produced when this method is used, and these samples improve the model’s performance in classification tasks. Rigorously tested through a combination of qualitative and quantitative evaluations, the introduced data augmentation technique improves the performance of terrain classification models, especially with limited data samples.