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Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission.

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    This study introduces a novel Structural Optimization Transmission Network (SOT-Net) for land-cover classification using hyperspectral image (HSI) and light detection and ranging (LiDAR) data. SOT-Net enhances complementary data utilization for improved accuracy, even with limited training samples.

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    Area of Science:

    • Remote Sensing
    • Geospatial Data Analysis
    • Machine Learning for Earth Observation

    Background:

    • Multisource remote sensing data, like hyperspectral image (HSI) and light detection and ranging (LiDAR), offer complementary information for land-cover classification.
    • Existing methods struggle with effectively transmitting structural information and aligning physical properties between HSI and LiDAR data, limiting their collaborative potential.
    • Redesigned information collaboration and redundancy exclusion are needed to strengthen semantic relatedness in multisource data fusion.

    Purpose of the Study:

    • To propose a novel framework, the Structural Optimization Transmission Network (SOT-Net), for enhanced collaborative land-cover classification using HSI and LiDAR data.
    • To improve the effective utilization of complementary information from different sensor sources by addressing structural information transmission and physical property alignment.
    • To enhance the robustness and accuracy of land-cover classification, particularly with limited training data.

    Main Methods:

    • Development of the Structural Optimization Transmission Network (SOT-Net) framework.
    • Integration of three key modules: cross-attention, dual-modes propagation, and dynamic structure optimization.
    • Incorporation of a self-alignment regularizer within the classification task to infer an optimal transmission plan.

    Main Results:

    • SOT-Net effectively leverages reflectance-specific information from HSI and structural details from LiDAR data.
    • The framework demonstrates consistent outperformance compared to baseline methods across three benchmark remote sensing datasets.
    • Satisfactory classification results were achieved even when using small-size training samples.

    Conclusions:

    • The proposed SOT-Net framework significantly enhances the complementary utilization of HSI and LiDAR data for land-cover classification.
    • The network architecture effectively addresses limitations in structural information transmission and physical property alignment.
    • SOT-Net offers a robust and accurate solution for land-cover classification, proving effective even in data-scarce scenarios.