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NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image

Yuanchao Su, Lianru Gao, Mengying Jiang

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    Summary
    This summary is machine-generated.

    This study introduces a new hyperspectral image classification method, normalized spectral clustering with kernel-based learning (NSCKL), to capture both local and global correlations for improved accuracy. NSCKL effectively classifies non-contiguous regions, outperforming existing methods.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Hyperspectral image (HSI) classification faces challenges in capturing non-local correlations due to reliance on local spatial-spectral information.
    • Existing spatial-spectral classification (SSC) methods often neglect correlations in non-Euclidean spaces.

    Purpose of the Study:

    • To develop a novel semisupervised HSI classification approach that integrates local and global correlations.
    • To enhance HSI classification performance by addressing limitations of existing SSC methods.

    Main Methods:

    • A normalized spectral clustering (NSC) scheme is proposed, leveraging a manifold assumption to learn new features.
    • A kernel-based iterative filter (KIF) establishes graph vertices, connecting pixels based on initial correlations.
    • The NSC aggregates correlations in both Euclidean and non-Euclidean (manifold) spaces, generating clustered features for classification.

    Main Results:

    • The proposed normalized spectral clustering with kernel-based learning (NSCKL) method effectively aggregates local-to-global correlations.
    • NSCKL demonstrates superior performance in hyperspectral image classification compared to state-of-the-art approaches.
    • The method successfully captures correlations in non-contiguous regions and non-Euclidean spaces.

    Conclusions:

    • The developed NSCKL approach offers a robust solution for semisupervised HSI classification.
    • This method significantly improves classification accuracy by considering broader spatial-contextual information.
    • The study highlights the potential of manifold learning for advanced HSI analysis.