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A Multi-path Neural Network for Hyperspectral Image Super-Resolution

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12890))

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Abstract

The resolution of hyperspectral remote sensing images is largely limited by the cost and commercialization requirements of remote sensing satellites. Existing super-resolution methods for improving the spatial resolution of images cannot well integrate the correlation between spectral segments and the problem of excessive network parameters caused by high-dimensional characteristics. This paper studies a multipath-based residual feature learning method, which simplifies each part of the network into several simple and effective network modules to learn the spatial spectral features between different spectral segments. Through the designed multi-scale feature generation method based on wavelet transform and spatial attention mechanism, the non-linear mapping ability for features is effectively improved. The verification of three general hyperspectral data sets proves the superiority of this method compared with the existing hyperspectral SR methods.

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Acknowledgments

This research was supported by the Natural Science Foundation of China under Grant 61801359, Grant 61571345 and the Pre-Research of the “Thirteenth Five-Year-Plan” of China Grant 305020903.

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Authors and Affiliations

  1. State Key Laboratory of Integrated Service Network, Xidian University, Xi’an, 710071, China

    Jing Zhang & Yunsong Li

  2. School of Telecommunications Engineering, Xidian University, Xi’an, 710071, China

    Jing Zhang, Zekang Wan, Minhao Shao & Yunsong Li

  3. Guangzhou Institution of Technology, Xidian University, Guanzhou, 510555, China

    Jing Zhang

Authors
  1. Jing Zhang
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  2. Zekang Wan
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  3. Minhao Shao
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  4. Yunsong Li
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Corresponding author

Correspondence to Jing Zhang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Zhang, J., Wan, Z., Shao, M., Li, Y. (2021). A Multi-path Neural Network for Hyperspectral Image Super-Resolution. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_31

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  • DOI: https://doi.org/10.1007/978-3-030-87361-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87360-8

  • Online ISBN: 978-3-030-87361-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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