Discrepant Bi-Directional Interaction Fusion Network for Hyperspectral and LiDAR Data Classification
Liangliang Song, Zhixi Feng, Shuyuan Yang, Xinyu Zhang, Licheng Jiao
Abstract
In recent years, the joint classification approach of hyperspectral image (HSI) and light detection and ranging (LiDAR) data based on deep learning (DL) has received increasing attention. However, existing methods either lack interaction between heterogeneous features during feature extraction or treat them equally during the interaction, inevitably resulting in redundant information stacking and reaching the performance bottleneck. To this end, we propose a novel discrepant bi-directional interaction fusion network (DBIFNet) for the collaborative classification of HSI and LiDAR data. First, a discrepant bi-directional interaction module (DBDIM) is designed to establish correlations between heterogeneous features to enhance the respective feature learning. Furthermore, a cross-modal attention fusion module (CAFM) is developed to dynamically fuse multi-modal features, which can further improve classification performance. Extensive experiments on the Houston and Trento datasets demonstrate that the proposed DBIFNet can achieve competitive classification performance.