It has been proved that the fusion of hyperspectral and LiDAR data can effectively improve the performance of land-use classification. Hyperspectral data contain more information than LiDAR data but most recent models pay more attention to the design of feature fusion mechanisms. They use CNN which is not powerful enough in extracting spatialspectral features of hyperspectral data. In this paper, a simple yet effective model with parallel transformers is proposed. Transformer is a powerful tool for both feature extraction and feature fusion. One transformer acts as an hyperspectral image feature extractor while the other transformer is responsible for capturing crossmodal interactions. Experiments on Houston dataset and MUUFL Gulfport dataset demonstrate that the proposed model has significantly better performance than other state-of-the-art models.