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DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing

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Abstract

Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In particular, the Autoencoder (AE) model, as a baseline network for unmixing, performs well in Hyperspectral Unmixing (HU) by automatically learning a new representation and recovering original data. However, patch-wise AE based architecture, which incorporates both spectral and spatial information through convolutional filters may blur the abundance maps due to the fixed kernel shape of the used window size. To cope with the above issue, we propose in this paper a novel methodology based on graph DL called DNGAE. Unlike the pixel-wise or patch-wise Convolutional AE (CAE), our proposed method incorporates the complementary spatial information based on graph spectral similarity. A neighborhood graph based on band correlations is firstly constructed. Then, our method attempts to aggregate similar spectra from the neighboring pixels of a target pixel. Consequently, this leads to better quality of both extracted endmembers and abundances. Extensive experiments performed on two real HSI benchmarks confirm the effectiveness of our proposed method compared to other DL models.

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Author information

Authors and Affiliations

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

    Refka Hanachi & Imed Riadh Farah

  2. CRIStAL Laboratory, University of Lille, Sciences et technologies, Bâtiment Esprit, 59655, Villeneuve-d’Ascq, France

    Akrem Sellami

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

    Imed Riadh Farah

  4. Grenoble INP, GIPSA-Lab Univ. Grenoble Alpes, CNRS, 38000, Grenoble, France

    Mauro Dalla Mura

Authors
  1. Refka Hanachi

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  2. Akrem Sellami

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  3. Imed Riadh Farah

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  4. Mauro Dalla Mura

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Corresponding author

Correspondence toRefka Hanachi.

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. Universität Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Hanachi, R., Sellami, A., Farah, I.R., Mura, M.D. (2023). DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing. In: Nguyen, N.T.,et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_7

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