Dual-Branch Soft Attention Network With Multiscale Feature Interaction for Hyperspectral and LiDAR Data Classification

In recent years, remote sensing (RS) data have become increasingly diversified due to the continuous innovation of sensors, communications, computers, and other technologies. The use of multimodal data for Earth observation missions has become a crucial research topic. Compared with single-source RS data, the fusion of multi-souce RS data can obtain more comprehensive information for categorizing scenes. However, multi-source RS images fusion classification usually requires complex feature extraction and fusion, building a suitable network complexity to facilitate heterogeneous information exchange and avoid substantial redundancy is a significant challenge. To overcome these limitations, we introduce a lightweight dual-branch soft-attention classification framework, which designs the multi-scale feature interaction module for collaborative HSI-LiDAR classification. Compared with other cutting-edge models, the proposed framework is compact and deeply integrates multi-modality heterogeneous characteristics. The multi-scale feature interaction module consists of a multi-scale information fusion pattern and a soft attention module, which can effectively extract hierarchical information and improve the heterogeneous feature representation. In addition, the Transformer module adopts weight-sharing to process features from different branches, effectively improve both parameter reduction and the powerful modeling capability of long-range dependencies. To validate the efficacy and advantages of our proposed framework, extensive experiments were performed across three datasets. The results indicate superior performance compared to current state-of-the-art approaches.