Volume 32 Issue 3
May  2023
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LI Yanshan, CHEN Shifu, LUO Wenhan, et al., “Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 415-428, 2023, doi: 10.23919/cje.2021.00.081
Citation: LI Yanshan, CHEN Shifu, LUO Wenhan, et al., “Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 415-428, 2023, doi: 10.23919/cje.2021.00.081

Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network

doi: 10.23919/cje.2021.00.081
Funds:  This work was partially supported by the National Natural Science Foundation of China (61771319, 61871154), the Natural Science Foundation of Guangdong Province (2017A030313343, 2019A1515011307), and the Shenzhen Science and Technology Project (JCYJ20180507182259896)
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  • Author Bio:

    Yanshan LI received the M.S. degree from the Zhejiang University of Technology in 2005 and the Ph.D. degree from the South China University of Technology, China, in 2015. He is currently an Associate Professor with the ATR National Key Laboratory of Defense Technology, Shenzhen University, China. His research interests include computer vision, machine learning, and image analysis. (Email: lys@szu.edu.cn)

    Shifu CHEN received the B.E. degree in School of Computer, Electronics and Information from Guangxi University, Nanning, China, in 2018. He is currently pursuing the M.S. degree in information and engineering from Shenzhen University, Shenzhen, China. He is a member of the ATR National Key Laboratory of Defense Technology, Shenzhen University. His research interests include intelligent information processing, video processing, and pattern recognition

    Wenhan LUO received the B.E. degree from Huazhong University of Science and Technology, China, in 2009, M.E. degree from Institute of Automation, Chinese Academy of Sciences, China, in 2012, and Ph.D. degree from Imperial College London, UK, in 2016. His research interests include several topics in computer vision and machine learning, such as motion analysis (especially object tracking), image/video quality restoration, object detection and recognition, and reinforcement learning

    Li ZHOU received the B.E. degree in automation from South-Central University for Nationalities, Wuhan, China, in 2018. He is currently pursuing the M.S. degree in information and engineering from Shenzhen University, Shenzhen, China. He is a member of the ATR National Key Laboratory of Defense Technology, Shenzhen University. His research interests include intelligent information processing, video processing, and pattern recognition

    Weixin XIE received the degree from Xidian University, Xi’an. He was a Faculty Member with Xidian University in 1965. From 1981 to 1983, he was a Visiting Scholar at the University of Pennsylvania, USA. In 1989, he was a Visiting Professor with the University of Pennsylvania. He is currently with the School of Information Engineering, Shenzhen University, China. His research interests include intelligent information processing, fuzzy information processing, image processing, and pattern recognition

  • Received Date: 2021-03-01
  • Accepted Date: 2021-12-07
  • Available Online: 2022-06-08
  • Publish Date: 2023-05-05
  • Constrained by the physics of hyperspectral sensors, the spatial resolution of hyperspectral images (HSI) is low. Hyperspectral image super-resolution (HSI SR) is a task to obtain high-resolution hyperspectral images from low-resolution hyperspectral images. Existing algorithms have the problem of losing important spectral information while improving spatial resolution. To handle this problem, a spatial-spectral feature extraction network (SSFEN) for HSI SR is proposed in this paper. It enhances the spatial resolution of the HSI while preserving the spectral information. The SSFEN is composed of three parts: spatial-spectral mapping network, spatial reconstruction network, and spatial-spectral fusing network. And a joint loss function with spatial and spectral constraints is designed to guide the training of the SSFEN. Experiment results show that the proposed method improves the spatial resolution of the HSI and effectively preserves the spectral information simultaneously.
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