Fractional Fourier-Enhanced Fusion Network Based on Pareto Optimization for Hyperspectral and LiDAR Data Classification

In recent years, the utilization of hyperspectral images (HSIs) and light detection and ranging (LiDAR) for collaborative classification has emerged as a significant research direction in Earth observation tasks, with diverse joint classification algorithms showing promising performance using varying network architectures. However, these methodologies infrequently address the challenge of fusion arising from the substantially larger volume of HSI feature information compared to LiDAR features. Moreover, the effective learning of HSI and LiDAR features while mitigating modality conflicts remains an area that necessitates further investigation. As such, a fractional Fourier-Enhanced fusion network (FrFENet) based on Pareto optimization is proposed for HSI and LiDAR data classification. To address the disparity in information volume between modalities, a weighted fractional Fourier-Enhanced fusion (WFrFEF) module is introduced, which applies a weighted fractional Fourier transform (WFrFT) to HSI features, enhancing their representations and facilitating balanced fusion with LiDAR features. Furthermore, a Pareto-based soft optimization strategy, HLPareto, is designed to balance learning rates across HSI and LiDAR features in a dual-branch network, effectively avoiding optimization conflicts. Additionally, a spatial-spectral integration module (SSIM) and an elevation information enhancement module (EIEM) are developed to improve feature extraction. The SSIM enables effective spatial-spectral fusion by facilitating token-level interactions, while the EIEM enhances elevation feature representation, preserving spatial geometric information in LiDAR data. Extensive experiments and comparative analyses conducted on three widely utilized HSI and LiDAR datasets have shown that the proposed FrFENet exhibits superior classification performance.