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    This study introduces a novel tensor-based subspace clustering model for hyperspectral band selection (BS). The method effectively captures multi-mode correlations, improving processing efficiency and performance over existing matrix-based approaches.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Data Science

    Background:

    • Hyperspectral image (HSI) band selection (BS) is crucial for reducing spectral dimensionality and enabling efficient processing.
    • Existing unsupervised BS methods often use matrix-based models, neglecting multi-mode correlations and operating in high-dimensional raw data spaces, leading to inefficiency and reduced robustness.
    • These limitations hinder the effective extraction of intrinsic band structures and correlations within HSI data.

    Purpose of the Study:

    • To propose a novel tensor-based subspace clustering model for hyperspectral band selection (BS).
    • To address the limitations of existing matrix-based methods by effectively capturing multi-mode correlations and operating in a learned low-dimensional latent space.
    • To improve the efficiency and robustness of hyperspectral band selection through a unified tensor framework.

    Main Methods:

    • Developed a tensor-based subspace clustering model utilizing Tucker decomposition to jointly encode multi-mode correlations of HSI.
    • Incorporated heterogeneous regularizations (HRs) on factor matrices, considering local and global properties across HSI dimensions.
    • Learned band correlations in a unified low-dimensional latent feature space derived from spatial dimension projections, avoiding raw data space limitations.

    Main Results:

    • The proposed tensor-based model effectively captures joint correlations across spectral and spatial modes of HSI data.
    • Heterogeneous regularizations facilitate learning of intrinsic band cluster structures in low-dimensional subspaces.
    • The model demonstrates computationally efficient processing by learning in a unified latent feature space.
    • Experimental results show improved performance compared to state-of-the-art unsupervised BS methods on benchmark datasets.

    Conclusions:

    • The proposed tensor-based subspace clustering model offers a superior approach for hyperspectral band selection compared to traditional matrix-based methods.
    • The model's ability to leverage multi-mode correlations and learn in a latent space enhances efficiency and robustness.
    • This work provides a significant advancement in unsupervised hyperspectral band selection, paving the way for more effective HSI data analysis.