AF ² DN: Attention-Guided Frequency Feature Decomposition Network for Hyperspectral and LiDAR Data Classification

Transformers have gained significant attention in multimodal remote sensing fusion due to their strong global context modeling capability. Although Transformer-based methods excel at processing high-dimensional spectral sequences and joint spatial-spectral information, most current research remains focused on the spatial domain. Consequently, the exploration of frequency-domain features—particularly implicit frequency representations—is often neglected. Moreover, efficiently fusing multimodal data features while emphasizing more discriminative information remains a challenging task. To address these challenges, this paper proposes an Attention-guided Frequency Feature Decomposition Network (AF<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>DN) for Hyperspectral and LiDAR Data Classification. First, a Transformer-based Frequency Feature Decomposition(TFFD) method is proposed, employing window attention to capture distinct directional frequency components from multimodal remote sensing data. Through this approach, low-frequency components are utilized to characterize global structural information, while various high-frequency components are employed to extract diverse texture and edge features. Second, an Attention Frequency Modulation(AFM) module is developed, incorporating a weight learning matrix in the frequency domain. This matrix is designed to selectively amplify and suppress different frequency components, thereby reducing data redundancy resulting from frequency feature decomposition. Finally, an adaptive Multimodal Same-Frequency Feature Fusion (AMSF<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) module is designed to achieve cross-modal feature integration at identical frequency bands. Extensive experiments are conducted on three benchmark datasets, and the results demonstrate that the proposed framework outperforms existing state-of-the-art methods while exhibiting stronger adaptability in complex environments.