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  3. Di3cl: Contrastive Learning With Dynamic Instances And Contour Consistency For Sar Land-cover Classification Foundation Model.
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  3. Di3cl: Contrastive Learning With Dynamic Instances And Contour Consistency For Sar Land-cover Classification Foundation Model.

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

Zhongle Ren, Hui Ding, Kai Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 6, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    A new foundation model for Synthetic Aperture Radar (SAR) land-cover classification reduces reliance on labeled data. The Dynamic Instance and Contour Consistency Contrastive Learning (DI3CL) framework improves accuracy and generalization for diverse mapping tasks.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Supervised learning methods for SAR land-cover classification require extensive labeled datasets, limiting scalability and adaptability.
    • Existing approaches struggle with generalization across diverse application scenarios and geographic regions.
    • A need exists for a general-purpose foundation model to streamline SAR land-cover classification development.

    Purpose of the Study:

    • To develop a general-purpose foundation model for SAR land-cover classification.
    • To introduce a novel pre-training framework, Dynamic Instance and Contour Consistency Contrastive Learning (DI3CL), to enhance model performance.
    • To improve the robustness and generalization capabilities of SAR land-cover classification models.

    Main Methods:

  • Developed a Dynamic Instance and Contour Consistency Contrastive Learning (DI3CL) pre-training framework.
  • Incorporated a Dynamic Instance (DI) module for enhanced global contextual awareness and a Contour Consistency (CC) module for improved structural discrimination.
  • Constructed a large-scale dataset (SARSense) with 460,532 SAR images for comprehensive feature capture.

    • Main Results:

    • The DI3CL framework demonstrated superior performance compared to existing methods in extensive experiments.
    • The foundation model showed strong generalization capabilities across various SAR land-cover classification tasks, including mapping, water detection, and road extraction.
    • Pre-trained weights and code are publicly available, facilitating further research and application.

    Conclusions:

    • The proposed DI3CL foundation model effectively addresses the limitations of supervised learning in SAR land-cover classification.
    • The DI3CL framework significantly enhances model robustness, generalization, and structural discrimination.
    • The developed foundation model serves as a robust cornerstone for accelerating diverse downstream SAR land-cover classification applications.