A Transformer-Enhanced Encoder-Decoder Network For Unsupervised Heterogeneous Remote Sensing Image Change Detection

The development of satellite and airborne sensor technologies has resulted in a wealth of multisource remote sensing images, which have great potential for precise analysis and monitoring of the Earth's surface. Despite recent advancements in change detection (CD) on multitemporal remote sensing images, there are still many challenges in effectively integrating multimodal remote sensing data in the CD task. To address these challenges, this paper proposes a novel unsupervised Transformer-enhanced Encoder-Decoder CD (namely ETDCD) framework for heterogeneous remote sensing images. The proposed framework focuses on fusion of multimodal features for achieving high-precision CD results without relying on high-quality manual labels. Experimental results demonstrate the superiority of the proposed ETD-CD network for multimodal remote sensing image CD.