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    This study introduces the Representation-enhanced Status Replay Network (RSRNet) to improve multisource remote-sensing image classification. RSRNet addresses representation and classifier bias, enhancing feature representation and fusion for superior accuracy.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Deep learning excels in multisource remote-sensing image classification but faces challenges like representation bias, classifier bias, and imbalanced fusion information.
    • These issues limit classification accuracy by hindering feature extractor optimization and full utilization of complementary multisource data.

    Purpose of the Study:

    • To propose a novel network, the Representation-enhanced Status Replay Network (RSRNet), to overcome limitations in deep learning for multisource remote-sensing image classification.
    • To enhance feature representation, reduce bias, and improve information interaction during data fusion.

    Main Methods:

    • Implemented a dual augmentation strategy (modal and semantic) to improve feature representation transferability and discreteness, mitigating representation bias.
    • Introduced a status replay strategy (SRS) to regulate classifier learning and optimization, alleviating classifier bias and stabilizing decision boundaries.
    • Employed a cross-modal interactive fusion (CMIF) method to jointly optimize parameters and enhance information interaction among multisource data branches.

    Main Results:

    • RSRNet demonstrated superior performance in multisource remote-sensing image classification across three datasets.
    • Quantitative and qualitative analyses confirmed the effectiveness of RSRNet compared to existing state-of-the-art methods.
    • The proposed methods successfully reduced representation and classifier bias and improved cross-modal information fusion.

    Conclusions:

    • RSRNet effectively addresses key challenges in deep learning for multisource remote-sensing image classification.
    • The network's novel augmentation, bias mitigation, and fusion strategies lead to significant performance improvements.
    • RSRNet offers a promising approach for advancing the field of remote-sensing image analysis.