Heterogeneous Contrastive Graph Fusion Network for Classification of Hyperspectral and LiDAR Data

In recent years, the rapid advancement of multisensory platforms has significantly increased the availability of multisource remote sensing data, facilitating its systematic application to various tasks. The joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data remains a critical research topic, with a key challenge being the effective extraction and integration of complementary information from multisource remote sensing data. However, existing graph convolutional networks (GCNs)-based methods often fail to account for the heterogeneous topological relationships between HSI and LiDAR. Moreover, the discriminative power of HSI and LiDAR features extracted by existing methods is insufficient. In addition, existing methods are unable to fully exploit the rich self-supervised information present in the local neighborhood. To address these limitations, we propose a heterogeneous contrastive graph fusion network (HCGFN) for the joint classification of HSI and LiDAR data. First, we propose a branch enhancement module (BEM) to enhance the discriminative power of HSI and LiDAR. Second, a contrastive learning module (CLM) is introduced to effectively align HSI and LiDAR representations. Finally, we propose a dynamic heterogeneous graph structure learning module (DHGSLM) to model heterogeneous relationships and achieve efficient interaction and effective fusion between HSI and LiDAR. The extensive experimental results on three benchmark datasets indicate the effectiveness of the proposed HCGFN compared with other state-of-the-art methods. Specifically, under limited training samples, the proposed HCGFN outperformed state-of-the-art methods in overall accuracy (OA) by 5.10%, 2.46%, and 8.79% on datasets Trento, MUUFL, and Houston2013, respectively.