Multi-Excitation Enhanced Multi-Feature Fusion Network for Hyperspectral and LiDAR Data Classification

With the development of multi-modal technology, hyperspectral image (HSI) and light detection and ranging (LiDAR) data has achieved remarkable results in land use and land cover (LULC) classification. Recently, many deep learning based feature extraction and fusion methods have improved the classification performance of LULC tasks. However, most of these methods use a single feature extractor and do not fully utilize the information of HSI and LiDAR data. Moreover, directly fusing various features obtained from feature extractor can lead to feature redundancy, resulting in model overfitting. In this paper, we develop a three-branch excitation network, named TBENet. The three branches extract spectral features, spatial features and elevation features respectively. And an excitation block is used to reduce feature redundancy and improve the generalization of the model. Contrast experiments on Houston dataset show that our proposed method outperforms other state-of-the-art methods, and ablation experiments demonstrate the effectiveness of each block.