WMCFN: wavelet-enhanced multiscale cross-modal fusion network for hyperspectral and LiDAR data fusion

The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has become a crucial topic in remote sensing, as these modalities provide highly complementary spectral and structural information. However, effectively integrating their heterogeneous characteristics while maintaining discriminative and balanced representations remains a fundamental challenge. We propose a wavelet-enhanced multiscale cross-modal fusion network (WMCFN) to address this issue. Compared with fixed wavelet decomposition strategies, WMCFN incorporates dataset-specific wavelet selection and energy-aware subband modulation to improve frequency representation quality. The framework is designed to capture both global spectral–spatial structures and local detail variations, enabling more comprehensive multimodal representation learning. Extensive experiments on two benchmark datasets demonstrate that the proposed method consistently outperforms existing approaches in classification accuracy and generalization. Beyond performance gains, we provide insights into adaptive multimodal feature fusion and contribute to advancing the interpretability and robustness of HSI–LiDAR joint analysis.