Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) has been increasingly utilized for fine-scale surface characterization and quantitative retrieval due to its capability of capturing dense spectral information at ultra-high spatial resolution. However, UAV-HSI analysis remains challenging due to high dimensionality, noise and within-class variability, as well as limited cross-flight consistency under varying acquisition conditions. Deep learning (DL) has therefore attracted growing attention by enabling spectral-spatial representation learning and more robust inference under residual degradations and domain shifts. This review summarizes DL approaches for UAV-HSI analytics and organizes the literature along a complete workflow, from imaging principles, preprocessing, and correction to DL architectures, core tasks, and representative applications, to provide guidance for future research and applications. The reviewed papers demonstrate that DL exhibits great potential and a promising future in UAV-HSI analysis.
Zhao et al. (Fri,) studied this question.