Dual-Feature Attention Hybrid GCN Mamba Network for Joint Hyperspectral and LiDAR Classification

Hyperspectral images (HSIs) and light detection and ranging (LiDAR) data provide complementary spectral-spatial and elevation information, respectively, whose fusion can significantly improve classification accuracy. However, their inherent heterogeneity challenges effective spectral-geospatial integration. Although convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer models have advanced multimodal remote sensing classification, each shows distinct limitations. CNNs excel in spatial feature aggregation but lack global context, whereas RNNs and Transformers, despite capturing long-range spectral features, face issues such as computational inefficiency. To address these limitations, we propose a dual-feature attention hybrid graph convolutional network (GCN) Mamba network (DAHGMN) for joint HSI and LiDAR classification. Specifically, multimodal image cubes are first extracted by a CNN to obtain initial features. Subsequently, a dual-feature attention (DA) module is introduced to adaptively recalibrate spectral and spatial feature weights, enhancing discriminability. Furthermore, we propose a hybrid GCN Mamba (HGM) module with both low parameter complexity and time complexity, which combines the local geometric modeling capability of GCNs with the global long-range dependency modeling of Mamba’s state-space model (SSM). A probability-based decision fusion strategy is employed to integrate multi-level classification results, achieving an efficient combination of the spatial-spectral contextual features. Extensive experiments on three benchmark HSI-LiDAR datasets demonstrate that DAHGMN achieves superior classification accuracy while significantly reducing parameter complexity compared to state-of-the-art methods. The implementation code is publicly available at https://github.com/RogsXie/DAHGMN.