Bridging HSI and LiDAR Data With Frequency-Domain Hierarchical Fusion for Enhanced Classification

Remote sensing data from hyperspectral imaging (HSI) and light detection and ranging (LiDAR) provide complementary perspectives for terrain and object analysis. However, existing methods for multimodal data fusion primarily focus on spatial-domain feature alignment, often overlooking the potential of frequency-domain information to enhance classification accuracy. To bridge this gap, we introduce the frequency-domain hierarchical perception fusion network (FHPF-Net), a novel framework for precise classification of remote sensing images. This network leverages both spatial-domain information and frequency-domain information and provides a new perspective for heterogeneous data integration. To extract and utilize frequency-domain features, we propose the high-to-low spectral separation and mining (HLSSM) module, which isolates high-frequency details such as edges and textures from low-frequency structural patterns in HSI and LiDAR data. This separation facilitates targeted feature extraction (FE) while preserving crucial contextual information. Additionally, we introduce the hierarchical superimposed multidomain information fusion (HSMIF) module, which employs a multilevel fusion strategy to integrate spatial- and frequency-domain features, ensuring consistency and complementarity between the two data sources. Finally, we introduce a learnable voting pre-label fusion (LVPF) strategy to effectively integrate multibranch outputs, enhancing classification performance and model robustness. The proposed FHPF-Net effectively captures diverse responses across heterogeneous data types, enabling robust classification in complex environments. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods.