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BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing

Yi Zou, Jiahui Qu, Wenqian Dong

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 29, 2026
    PubMed
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    This study introduces a new prompt-based framework for class-incremental learning in multi-source remote sensing image classification. The method enhances stability and plasticity for continuous learning across diverse data sources.

    Area of Science:

    • Computer Science
    • Remote Sensing
    • Artificial Intelligence

    Background:

    • Class-incremental learning (CIL) is crucial for expanding category recognition in remote sensing without forgetting past data.
    • Existing CIL methods often fail in multi-source scenarios by not leveraging complementary information across different data origins.
    • Remote sensing applications require adaptive classification systems capable of long-term learning.

    Purpose of the Study:

    • To develop a novel prompt-based framework for class-incremental learning specifically designed for multi-source remote sensing image classification.
    • To address the limitations of single-source CIL methods in exploiting multi-modal data.
    • To enhance both model stability and plasticity for effective incremental learning in complex remote sensing environments.

    Main Methods:

    Related Experiment Videos

    • Proposed a Bidirectional Cross-Modal Prompt Tuning (BiCM-PT) module for dynamic prompt selection and preservation of cross-modal relationships.
    • Introduced a Prompt-Guided Knowledge Aggregator (PGKA) to guide feature aggregation and extract discriminative multi-modal representations.
    • Utilized prompt-based learning to improve stability by freezing modality relation projectors and plasticity for new-class learning.

    Main Results:

    • Demonstrated effective and stable class-incremental learning in multi-source remote sensing environments.
    • Achieved a balance between stability (preserving old knowledge) and plasticity (learning new knowledge).
    • Validated the approach's effectiveness on three real-world remote sensing benchmarks.

    Conclusions:

    • The proposed prompt-based framework significantly improves class-incremental learning performance in multi-source remote sensing.
    • The BiCM-PT and PGKA modules effectively handle the challenges of catastrophic forgetting and multi-modal data integration.
    • The method offers a promising solution for long-term adaptive classification in remote sensing applications.