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    This study introduces a new hyperspectral unmixing method using a data-guided map (DgMap) for adaptive sparsity constraints. This approach improves spectral basis estimation and unmixing accuracy in complex hyperspectral data.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Signal Processing

    Background:

    • Hyperspectral unmixing is crucial for analyzing spectral signatures and material composition.
    • Unsupervised hyperspectral unmixing is challenging due to unknown spectral bases and abundances.
    • Existing methods often apply uniform constraints, leading to suboptimal solutions.

    Purpose of the Study:

    • To develop a novel unsupervised hyperspectral unmixing method using adaptive sparsity.
    • To introduce a data-guided map (DgMap) for pixel-wise constraint adjustment.
    • To improve the accuracy and robustness of hyperspectral unmixing.

    Main Methods:

    • Proposed a sparsity-based hyperspectral unmixing method.
    • Introduced a data-guided map (DgMap) to learn pixel-specific mixing levels.
    • Applied adaptive l(p) (0 < p < 1) constraints guided by the DgMap.
    • Developed an optimization scheme with convergence proof.

    Main Results:

    • The DgMap effectively guides spectral bases toward pixels with sparse constraints.
    • The adaptive constraint approach accommodates varying pixel mixing levels.
    • Experimental results demonstrate the feasibility and high performance of the DgMap method.
    • Achieved high-quality unmixing results across multiple datasets.

    Conclusions:

    • The proposed DgMap-based hyperspectral unmixing method offers a significant advancement.
    • Adaptive sparsity constraints improve unmixing accuracy in complex scenarios.
    • The method provides a robust and effective solution for unsupervised hyperspectral unmixing.