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    This study introduces CorrFusion, a novel module for remote sensing scene change detection. It enhances land-use classification by effectively fusing correlated features from bi-temporal imagery, improving accuracy on large-scale datasets.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Geospatial Analysis

    Background:

    • Land-use transitions in urban areas are crucial for urban planning and environmental monitoring.
    • Existing scene change detection methods often overlook temporal feature correlations and are evaluated on limited datasets.

    Purpose of the Study:

    • To develop an effective method for classifying multi-temporal land-use categories and detecting semantic scene-level changes in urban remote sensing imagery.
    • To address the limitations of existing methods by focusing on temporal correlations and large-scale datasets.

    Main Methods:

    • Proposed a CorrFusion module to fuse highly correlated components in bi-temporal feature embeddings extracted using deep convolutional networks.
    • Implemented cross-temporal fusion based on instance-level correlation computed in a lower-dimensional space.
    • Introduced an efficient and stable objective function for temporal correlation calculation and provided backpropagation gradient derivations.

    Main Results:

    • Demonstrated significant improvements in multi-temporal scene classification and scene change detection using the proposed CorrFusion module.
    • Validated the effectiveness of the approach on a newly presented large-scale scene change detection dataset with diverse semantic categories.

    Conclusions:

    • The CorrFusion module offers a robust and effective solution for analyzing land-use dynamics in urban environments using remote sensing data.
    • The developed method enhances the accuracy and reliability of scene change detection, contributing to better urban monitoring and management.