Multimodal Prompt Tuning for Hyperspectral and LiDAR Classification

The joint classification of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data holds significant importance for various practical uses, including urban mapping, mineral prospecting, and ecological observation. Achieving robust and transferable feature representations is essential to fully leverage the complementary properties of HSI and LiDAR modalities. However, existing methods are often constrained to scene-specific training and lack generalizability across datasets, limiting their discriminative power. To tackle this challenge, we introduce a new dual-phase approach for the combined classification of HSI and LiDAR data. Initially, a transformer-driven network is trained on various HSI-only datasets to extract universal spatial–spectral features. In the second stage, LiDAR data is incorporated as a task-specific prompt to adapt the model to HSI-LiDAR scenes and enable effective multimodal fusion. Through extensive testing on three benchmark datasets, our framework proves highly effective, outperforming all competing approaches.