Dual-Branch Feature Interaction Network for Coastal Wetland Classification Using Sentinel-1 and 2

The combination of multispectral image (MSI) and synthetic aperture radar (SAR) data has made certain progress in coastal wetland classification. How to realize the interactive fusion between the two data and make full use of their fusion characteristics becomes challenging. However, the existing joint classification methods neglect interaction information between features and underutilize fusion features. Therefore, this article proposes a dual-branch feature interaction network (DFI-Net) that joins MSI and SAR data for coastal wetland classification. The dual-branch independent structure of 3DCNN processing MSI and 2DCNN processing SAR is designed, which can effectively capture spectral-spatial features and polarization features. In addition, we develop two novel modules. The feature interaction fusion block is designed to enhance the complementarity between the features of the two kinds of data. This block employs a cross-agent attention mechanism to realize effective interaction between MSI and SAR features and adaptive fusion of contextual information from the two branches. Finally, a plug-and-play module channel-spatial transformer encode (CSTE) is proposed to improve the utilization rate of interactive fusion data. The CSTE utilizes two parallel transformers to deeply mine information in interactive fusion data and explore channel-spatial features across all dimensions to the maximum extent possible. The classification experiment is conducted on the Yellow River Delta coastal wetland dataset. The experimental results show that the overall accuracy of DFI-Net reaches 97.03%, which outperforms the performance of other competitive approaches. The effectiveness of DFI-Net provides a reference method for combining MSI and SAR for coastal wetland classification.