Review of Deep Learning Methods for Interpreting UAV-Borne Hyperspectral Imagery

‍ ‍With the rapid development of unmanned aerial vehicle （UAV） platforms and hyperspectral imaging technologies， UAV-based hyperspectral remote sensing has shown great promise in precision agriculture， ecological monitoring， and natural resource management. However， its practical application still faces several technical challenges， including unstable data acquisition， severe image distortion， and poor spectral consistency， necessitating systematic research and methodological optimization. In recent years， deep learning has gradually emerged as the core technological approach for intelligent interpretation in this field， owing to its advantages in automatic feature extraction and complex scenario modeling. To comprehensively outline recent research progress and technical trends， this paper presents a systematic review of UAV hyperspectral imaging systems， deep learning interpretation methods， and typical application areas. Special emphasis is placed on the evolution and implementation of deep learning in core tasks such as classification， segmentation， and target detection. Representative models—including convolutional neural networks， graph neural networks， and transformers—are thoroughly analyzed. The study highlights the high processing complexity and significant uncertainty of UAV hyperspectral data， primarily caused by illumination variations， attitude instability， and background interference. Current approaches remain limited by poor adaptability to varied scenarios， high demands for real-time performance， and the cost of extensive data preprocessing in real-world applications. Future research should focus on efficient feature representation， lightweight model architectures， and improving robustness across diverse environments. By systematically organizing mainstream deep learning approaches， this review summarizes representative techniques and interpretation strategies for key tasks， including target detection， classification， and segmentation， offering a theoretical foundation for continued optimization and practical implementation of UAV hyperspectral image analysis. In addition， the discussion of public datasets provides practical references to support experimental validation and facilitate further research in the field.