文献类型： 外文期刊

作者： Liu, Zhengyu ¹ ; Yuan, Xia ¹ ; Yang, Shuting ² ; Fu, Guanyiman ¹ ; Zhao, Chunxia ¹ ; Xiong, Fengchao ¹ ;

作者机构： 1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China

2.Ningxia Acad Agr & Forestry Sci, Inst Agr Econ & Informat Technol, Yinchuan 750002, Peoples R China

关键词： hyperspectral image; light detection and ranging (LiDAR); multimodal fusion; prompt tuning

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 16 期

页码：

收录情况： SCI

摘要： 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.