DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model

The recent publication of the DI3CL model marks a significant advancement in SAR land-cover classification, addressing the prevalent reliance on supervised learning methods that depend heavily on large labeled datasets. This dependency has limited the scalability and generalization of existing approaches. DI3CL introduces a Dynamic Instance module that enhances contextual awareness and a Contour Consistency module that focuses on the geometric contours of land-cover objects, improving structural discrimination. With a robust pre-training framework, DI3CL is designed to serve as a general-purpose foundation model, facilitating the development of various downstream applications. The model is trained on a large-scale dataset comprising 460,532 SAR images, which enhances its robustness and adaptability across different classification tasks. This innovation not only accelerates the deployment of SAR classification models but also opens avenues for more efficient and effective applications i…