Multimodal Quaternion Representation Network for Multisource Remote Sensing Data Classification

The effective integration and classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data is of great significance in Earth observation missions, which are confronted with challenges such as insufficient information utilization and feature heterogeneity. This article proposes a multimodal quaternion representation network (MMQRN) for multisource remote sensing (RS) data classification. Specifically, we first propose the multimodal quaternion representation (MMQR), which employs the orthogonal imaginary components of quaternions to model the complex nonlinear interactions among complementary features, thereby enabling their comprehensive fusion and utilization. Subsequently, we design a multimodal feature cross-fusion (MFCF) framework to integrate multisource, multimodal, and multilevel features adequately. Finally, we leverage the ability to capture long-term dependencies of transformers to design a quaternion convolutional transformer network (QCTN) for modeling global and local spatial-spectral information, respectively. Experiments conducted on three multisource RS datasets demonstrate the superior performance of the proposed MMQRN relative to other state-of-the-art classification methods.