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    A new framework, GCCQTNet, effectively classifies multisource remote sensing data by addressing data heterogeneity. It leverages cross-memory transformers and comparative learning for superior fusion and classification accuracy.

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    Area of Science:

    • Remote Sensing
    • Geospatial Artificial Intelligence
    • Data Fusion

    Background:

    • Multisource remote sensing data classification is hindered by inherent data heterogeneity.
    • Existing deep learning fusion methods often fail to overcome this heterogeneity, limiting performance.
    • Exploiting complementarity between different data sources (e.g., HSI, SAR, LiDAR) is crucial for improved classification.

    Purpose of the Study:

    • To propose a novel multimodal joint classification framework, GCCQTNet, for multisource remote sensing data.
    • To effectively address the challenge of multimodal data heterogeneity.
    • To enhance the utilization of complementary information across different remote sensing data types.

    Main Methods:

    • Developed a three-branch structure for local and global feature extraction.
    • Introduced an Independent Squeeze-Expansion-like Fusion (ISEF) to mitigate heterogeneity.
    • Utilized a Cross-Memory Quaternion Transformer (CMQT) for complex feature relationship modeling.
    • Implemented Cross-Modality Comparative Learning (CMCL) for guided end-to-end training.

    Main Results:

    • GCCQTNet demonstrated superior performance compared to state-of-the-art methods on three public multisource remote sensing datasets.
    • The proposed framework effectively suppresses the negative impact of multimodal heterogeneity.
    • Enhanced feature fusion captured complex intramodality and intermodality relationships, leveraging complementarity.

    Conclusions:

    • GCCQTNet offers a robust solution for multisource remote sensing data classification.
    • The framework's novel components effectively handle data heterogeneity and exploit complementarity.
    • The results highlight the potential of GCCQTNet for advanced geospatial analysis.