CDTCL: Cross-Domain Remote Sensing Image Translation for Semantic Segmentation Leveraging Contrastive Learning
Ziyao Li, Zhengyi Lei, Mengjie Xie, Hong Ji, Yanzhang Li, Jun Zhu, Zhi Yang Gao · 2024
Although Deep Learning based methods for remote sensing (RS) image interpretation have reported promising results, the domain gap between RS images and the absence of sensor-specific labeled datasets result in the significant deterioration of well-trained models to adapt to new images. In practical applications, we propose an RS image translation method based on contrastive learning (CDTCL) to quickly achieve the data simulation conversion for different sensors and domains. Specifically, for unpaired images, we design a content contrastive loss for content consistency constraints and a style contrastive loss for swift alignment of appearance style. Additionally, we integrate the semantic segmentation model, a flexible model that can be retrained at the discretion of users, into the image translation framework to establish a complementary closed loop. Extensive experiments on aerial images, including visible and infrared images, verify that our method works effectively in cross-domain semantic segmentation and achieves the best performance.