S ² GCCFN: Shared-Specific Graph Construction-Oriented Crossmodal Fusion Network for HSI and LiDAR Data Joint Classification

The fusion of hyperspectral images (HSI) and light detection and ranging (LiDAR) data reflects strong complementary advantages, significantly enhancing the accuracy and robustness of land cover classification. However, inherent differences in sensor mechanisms and imaging principles lead to substantial heterogeneity in feature representation and scene characterization between these two modalities, posing challenges for effective multisource remote sensing (RS) fusion. To address this issue, a shared-specific graph construction oriented cross-modal fusion network (S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCCFN) is proposed for HSI and LiDAR data joint classification. Our framework facilitates deep feature interaction between heterogeneous HSI and LiDAR data to achieve modal homogenization, yielding a high-performance joint representation. The core motivation of this study is to capture one assimilation modality (AM) by discovering latent crossmodal mapping strategies from both HSI and LiDAR data simultaneously. Notably, S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCCFN mitigates modal heterogeneity while enhancing complementary information exchange. S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCCFN considers one dynamic graph mapping mechanism that adaptively integrates intramodal and crossmodal features comprehensively to align dual RS modalities and promote RS interaction. By establishing shared feature spaces, S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCCFN enhances intermodal consistency while preserving modality-specific characteristics, ultimately generating highly discriminative AMs for joint classification. Furthermore, S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCCFN incorporates an AM reconstruction decoder to jointly address modality reconstruction and classification. Experimental results demonstrate that S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>GCCFN outperforms state-of-the-art techniques, improving overall accuracy by 2.66% and 1.34% on average on both datasets, respectively.