MF-Mamba: A Multifrequency Mamba for the Joint Classification of HSI and LiDAR

In recent years, deep learning (DL) has achieved excellent performance on the joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. However, the existing DL methods often struggle to capture both global and local spatial features simultaneously, resulting in inadequate modeling of long- and short-range spatial dependencies. Moreover, the inherent heterogeneity of multisource data complicates effective fusion. To address these issues, we propose a multifrequency Mamba (MF-Mamba) network for the HSI and LiDAR joint classification. First, a Toeplitz fusion module (TFM) is designed to enable multilevel feature interaction between modalities, thereby reducing discrepancies. Second, a global-local Mamba (GL-Mamba) module is presented, which employs a frequency-space processing strategy. Specifically, the discrete wavelet transform (DWT) is first applied to decompose features into low- and high-frequency components. These components are then efficiently enhanced by Mamba before being reconstructed via inverse DWT (IDWT). Finally, to overcome the limitations of conventional fusion methods in capturing complex feature interdependencies, a multisource cross fusion (MSCF) module is proposed to integrate high-level semantic features from heterogeneous data. Extensive experiments demonstrate that MF-Mamba outperforms the state-of-the-art joint HSI-LiDAR classification networks, highlighting its superiority and potential. The source code will be made available at https://github.com/Dilingliao/MF-Mamba.