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  3. Tgmn: Two-stage Graph Convolutional Mamba Network For Hyperspectral Image Classification.
  1. Home
  3. Tgmn: Two-stage Graph Convolutional Mamba Network For Hyperspectral Image Classification.

Related Experiment Video

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Published on: December 15, 2023

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TGMN: Two-Stage Graph Convolutional Mamba Network for Hyperspectral Image Classification.

Yonghe Chu, Jun Cao, Junshi Xia

    IEEE Transactions on Neural Networks and Learning Systems
    |December 19, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    A novel two-stage graph convolutional mamba network (TGMN) efficiently classifies hyperspectral images (HSI) by sequentially extracting local and global features. This approach reduces computational complexity and improves accuracy compared to existing methods.

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    991

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Hyperspectral image (HSI) classification requires both local spectral and global spatial information.
    • Current methods using CNNs, GCNs, and Transformers often employ complex multibranch structures, leading to high computational costs and redundancy.

    Purpose of the Study:

    • To propose an efficient and effective method for HSI classification.
    • To address the limitations of existing multibranch models in terms of computational complexity and redundant information.

    Main Methods:

    • A two-stage graph convolutional mamba network (TGMN) is introduced for sequential local and global feature extraction.
    • The first stage uses GCNs on superpixel subgraphs with a DSFR module for intrasubgraph feature aggregation and redundancy reduction.
  • The second stage employs a Mamba network with RAPE for intersubgraph global dependency modeling and spatial context integration.

    • Main Results:

    • TGMN achieved high classification accuracies: 98.54% on Indian Pines, 98.30% on Dioni, and 96.94% on Honghu.
    • The proposed method demonstrates superior performance compared to state-of-the-art techniques.
    • TGMN significantly reduces computational cost while maintaining high classification accuracy.

    Conclusions:

    • The TGMN effectively models local and global features sequentially, overcoming the limitations of multibranch architectures.
    • The method offers an efficient and accurate solution for hyperspectral image classification.
    • TGMN presents a promising advancement in the field of HSI analysis.