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Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks

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Abstract

Hyperspectral image (HSI) classification benefits from effectively handling both spectral and spatial features. However, deep learning (DL) models, like graph convolutional networks (GCN), face challenges in computation time, overfitting, and less informative features. To address these challenges, we propose a novel method called MV-GRL (multi-view graph representation learning) for HSI classification using spectral–spatial graph neural networks. MV-GRL incorporates spectral and spatial features with the extended morphological profile (EMP) and employs a multi-view graph autoencoder (MVGAE) to learn a low-dimensional latent graph representation by fusing two input graphs: spectral features-based graph and spatial-based graph. This fusion enhances the discriminative power of the features. Additionally, we introduce a semi-supervised spectral–spatial GCN using the multi-view latent representation, leveraging labeled and unlabeled samples for improved classification performance. By leveraging both labeled and unlabeled data, our method effectively captures underlying relationships and enhances overall accuracy. Experimental results on the Indian Pines, Salinas, and Pavia University datasets demonstrate its competitive performance, achieving overall accuracy (OA) scores of 96.91%, 97.64%, and 98.88%, respectively, surpassing state-of-the-art (SOTA) models.

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Data availability

The datasets generated during and/or analyzed during the current study are available at [https://ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes].

Notes

  1. https://ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes.

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Funding

Funding information is not applicable/no funding was received.

Author information

Author notes

  1. Akrem Sellami, Imed Riadh Farah and Mauro Dalla Mura contributed equally to this work.

Authors and Affiliations

  1. RIADI Laboratory, ENSI, University of Manouba, 2010, Manouba, Tunis, Tunisia

    Refka Hanachi & Imed Riadh Farah

  2. Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, 59000, Lille, France

    Akrem Sellami

  3. ITI Department, IMT Atlantique, 655 Avenue du Technopôle, 29280, Plouzané, Paris, France

    Imed Riadh Farah

  4. Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), GIPSA-lab, 38000, Grenoble, France

    Mauro Dalla Mura

  5. Institut Universitaire de France (IUF), 75231, Paris, France

    Mauro Dalla Mura

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  1. Refka Hanachi
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  2. Akrem Sellami
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Correspondence to Refka Hanachi.

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Hanachi, R., Sellami, A., Farah, I.R. et al. Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks. Neural Comput & Applic 36, 3737–3759 (2024). https://doi.org/10.1007/s00521-023-09275-5

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