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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Published on: June 18, 2021

Spectral State Fusion Tree Mamba for Hyperspectral Image Classification.

Bing Tu, Zhenghao Hu, Bo Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Spectral State Fusion Tree Mamba (SSFTM) for hyperspectral image (HSI) classification. SSFTM improves spatial-spectral feature extraction and achieves superior accuracy over existing methods.

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    Published on: August 22, 2019

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Hyperspectral image (HSI) data present challenges in classification due to complex spatial structures and high-dimensional spectral information.
    • Traditional Mamba models face limitations in HSI classification, including restricted receptive fields, high computational costs, and suboptimal spatial-spectral relationship modeling.
    • Existing methods fail to adapt scanning paths based on spectral similarity and overlook inter-channel spectral feature extraction.

    Purpose of the Study:

    • To propose a novel Spectral State Fusion Tree Mamba (SSFTM) architecture for enhanced HSI classification.
    • To address the limitations of traditional Mamba in capturing spatial-spectral dependencies and spectral feature extraction.
    • To improve the efficiency and accuracy of hyperspectral image classification.

    Main Methods:

    • Introduced the Tree Scan (TS) mechanism to construct adaptive minimum spanning trees in spatial and spectral domains, optimizing spatial-spectral relationships.
    • Developed the Spectral State Fusion (SSF) mechanism using multi-layer one-dimensional dilated convolutions for inter-channel spectral feature extraction.
    • Implemented the SSFTM architecture for hyperspectral image classification tasks.

    Main Results:

    • The proposed SSFTM architecture achieved superior classification accuracy on multiple benchmark datasets compared to state-of-the-art (SOTA) methods.
    • SSFTM demonstrated effective joint feature extraction in both spatial and spectral domains.
    • The model exhibited acceptable computational complexity, making it a practical solution for HSI classification.

    Conclusions:

    • The SSFTM architecture effectively overcomes the limitations of traditional Mamba for HSI classification.
    • The novel TS and SSF mechanisms enable adaptive spatial-spectral relationship modeling and multi-scale spectral feature extraction.
    • SSFTM offers a promising approach for accurate and efficient hyperspectral image classification.