SwinTFNet: Dual-Stream Transformer With Cross Attention Fusion for Land Cover Classification

Land cover classification (LCC) is an important application in remote sensing data interpretation. As two common data sources, SAR images can be regarded as an effective complement to optical images, which will reduce the influence caused by single-modal data. But common LCC methods are focusing on designing advanced network architectures to process single-modal remote sensing data. Few works have been oriented toward improving segmentation performance through fusing multi-modal data. In order to deeply integrate SAR and optical features, we propose SwinTFNet, a dual-stream deep fusion network. Through the global context modeling capability of Transformer structure, SwinTFNet models teleconnections between pixels in other regions and pixels in cloud regions for better prediction in cloud regions. In addition, a Cross-Attention Fusion Module (CAFM) is proposed to fuse features from optical and SAR data. Experimental results show that our method improves greatly in the classification of clouded images compared with other excellent segmentation methods and achieves the best performance on multi-modal data.