<p>With the rapid advancement of remote sensing (RS) technology, the difficulty of acquiring multi-source RS data is significantly decreasing. Land use/land cover (LULC) classification methods that fuse hyperspectral (HS) image and light detection and ranging (LiDAR) have become a current research hotspot. However, current mainstream methods primarily focus on extracting the most salient features from HS image and LiDAR data. They emphasize the rich spectral information of HS image and the precise elevation information of LiDAR, often overlooking the important frequency-domain characteristics inherent in both data sources. Based on this, we propose a novel network framework called frequency coupled fusion convolutional neural network (FCFCNN), which aims to enhance classification performance by integrating frequency features from HS image and LiDAR data as supplementary information in the feature fusion process. The network is designed with a dual-branch structure. The first branch focuses on frequency information, employing a frequency-splitting module to separately extract high-frequency and low-frequency features from HS image and LiDAR data. Subsequently, the local enhanced position attention module (LEPAM) and coupling strategy are used to fuse these high-frequency and low-frequency features, respectively. The second branch focuses on learning the spectral-spatial features of HS image and the elevation features in LiDAR data. Subsequently, all features extracted from the two branches are fused at the feature level. Finally, they are combined with spectral-spatial and elevation information through decision-level fusion to achieve accurate classification. The experimental results on three real RS datasets demonstrate that our method exhibits better effectiveness and accuracy compared to existing technologies.</p>