Multiple Information Collaborative Fusion Network for Joint Classification of Hyperspectral and LiDAR Data

Joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) can simultaneously utilize rich spectral information and elevation information and has become a hot research topic in remote sensing (RS). Although many works have been proposed for this task, their performance cannot reach what we expected due to inadequate cross-modal feature learning and simple feature fusion. This article proposes a multiple information collaborative fusion network (MICF-Net) to overcome those limitations, which aims to leverage the essentially consistent spatial relationships and high-level semantic information in multimodal data to guide the extraction of multimodal fusion features. Specifically, MICF-Net first uses a simple two-branch convolutional neural network (CNN) for preliminary feature extraction. Then, a dual-branch cross-modal attention fusion transformer (CMAFT) is developed to mine global contextual content. By fusing the attention maps of two modalities and limiting their similarity, CMAFT can retain modality-specific information while achieving information interaction based on spatial relationships. Next, an adaptive mask modulation (AMM) module is designed to dynamically balance the learning rate of each modality to ensure the effectiveness of the features of all modalities. Finally, to mine the complementary information of HSI and LiDAR data, a semantic-guided feature fusion (SGFF) module is introduced. It achieves mutual guided learning by exchanging semantic information between two modalities. Positive experimental results counted on three popular HSI and LiDAR datasets demonstrate the effectiveness of the proposed MICF-Net. Our source codes are available at
https://github.com/TangXu-Group/Hyperspectral-Images-Classification/tree/main/MICF-Net
.