Skip to main content
SpringerLink
Log in

A Lightweight Hyperspectral Image Super-Resolution Method Based on Multiple Attention Mechanisms

  • Conference paper
  • First Online:
Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

Included in the following conference series:

  • 951 Accesses

Abstract

Hyperspectral images (HSI) are characterized by high spectral resolution but low spatial resolution, which is limited by the capabilities of imaging sensors. Due to the high-dimensional nature of HSIs and the correlation between spectra, existing super-resolution methods for HSIs suffer from excessive number of parameters and insufficient complementary information between spectra. This paper proposes a lightweight hyperspectral image super-resolution method based on multiple attention mechanisms, which simplifies each part of the network into a few simple yet effective network modules. Including a large kernel pixel attention (LKPA) network to extract shallow features from HSI. Efficient channel attention (ECA) is utilized to capture similar features across multiple channels. An Efficient Transformation Layer (ETL) network to extract deep features. A contextual incremental fusion (CIF) to exploring spectral feature information. Through large number of verifications on the two general hyperspectral datasets, the excellent experimental results achieved by our proposed method with very few model parameters demonstrate its superiority.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jalal, R., et al.: Toward efficient land cover mapping: an overview of the national land representation system and land cover map 2015 of Bangladesh. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 3852–3861 (2019)

    Google Scholar 

  2. Zhang, P., et al.: Monitoring of drought change in the middle reach of Yangtze River. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, pp. 4935–4938 (2018)

    Google Scholar 

  3. Goetzke, R., Braun, M., Thamm, H.P., Menz, G.: Monitoring and modeling urban land-use change with multitemporal satellite data. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, vol. 4, pp. 510–513 (2008)

    Google Scholar 

  4. Darweesh, M., Mansoori, S.A., Alahmad, H.: Simple roads extraction algorithm based on edge detection using satellite images. In: Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing, ICIVC, Xiamen, China, 5–7 July 2019, pp. 578–582 (2019)

    Google Scholar 

  5. Kussul, N., Shelestov, A., Yailymova, H., Yailymov, B., Lavreniuk, M., Ilyashenko, M.: Satellite agricultural monitoring in Ukraine at country level: world bank project. In: Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020, pp. 1050–1053 (2020)

    Google Scholar 

  6. Di, Y., Xu, X., Zhang, G.: Research on secondary analysis method of synchronous satellite monitoring data of power grid wildfire. In: Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA, Chongqing, China, 6–8 November 2020, pp. 706–710 (2020)

    Google Scholar 

  7. Liu, Y.N.: Development of hyperspectral imaging remote sensing technology. Natl. Remote Sens. Bull. 25(1), 439–459 (2021)

    Article  Google Scholar 

  8. Gomez, R.B., Jazaeri, A., Kafatos, M.: Wavelet-based hyperspectral and multispectral image fusion. In: Geo-Spatial Image and Data Exploitation II, pp. 36-42 (2001)

    Google Scholar 

  9. Wei, Q., Bioucas-Dias, J., Dobigeon, N., et al.: Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Trans. Geosci. Remote Sens. 53(7), 3658–3668 (2015)

    Article  Google Scholar 

  10. Yokoya, N., Yairi, T., Iwasaki, A.: Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Trans. Geosci. Remote Sens. 50(2), 528–537 (2011)

    Article  Google Scholar 

  11. Li, S., Dian, R., Fang, L., et al.: Fusing hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Trans. Image Process. 27(8), 4118–4130 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  12. Zheng, K., Gao, L., Liao, W., et al.: Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution. IEEE Trans. Geosci. Remote Sens. (2020)

    Google Scholar 

  13. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  14. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016, pp. 1646–1654 (2016)

    Google Scholar 

  15. Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 12–26 July 2017, pp. 1132–1140 (2017)

    Google Scholar 

  16. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2472–2481 (2018)

    Google Scholar 

  17. Hu, J., Li, Y., Xie, W.: Hyperspectral image super-resolution by spectral difference learning and spatial error correction. IEEE Geosci. Remote Sens. Lett. 14(10), 18251829 (2017)

    Article  Google Scholar 

  18. Li, Y., Zhang, L., Dingl, C., Wei, W., Zhang, Y.: Single hyperspectral image super-resolution with grouped deep recursive residual network. In: Proceedings of the IEEE 4th International Conference on Multimedia Big Data (BigMM), September 2018, pp. 1–4 (2018)

    Google Scholar 

  19. Jiang, J., Sun, H., Liu, X., et al.: Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery. arXiv preprint arXiv:2005.08752 (2020)

  20. Mei, S., Yuan, X., Ji, J., Zhang, Y., Wan, S., Du, Q.: Hyperspectral image spatial super-resolution via 3D full convolutional neural network. Remote Sens. 9(11) (2017)

    Google Scholar 

  21. Li, Q., Wang, Q., Li, X.: Mixed 2D/3D convolutional network for hyperspectral image super-resolution. Remote Sens. 12(10) (2020)

    Google Scholar 

  22. Zhao, H., Kong, X., He, J., Qiao, Y., Dong, C.: Efficient image super-resolution using pixel attention. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 56–72. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-67070-2_3

    Chapter  Google Scholar 

  23. Zhou, L., et al.: Efficient image super-resolution using vast-receptive-field attention. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13802, pp. 256–272. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25063-7_16

  24. Shi, J., Li, H., Liu, T., et al.: Image Super-Resolution using Efficient Striped Window Transformer. arXiv preprint arXiv:2301.09869 (2023)

  25. Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: Image restoration using swin transformer. In: IEEE International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  26. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: IEEE International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

Download references

Acknowledgements

this research was funded by the National Key R&D Program of China (grant number 2020YFA0713503), the Science and Technology Project of Hunan Provincial Natural Resources Department (grant number 2022JJ30561), the Scientific Research Project of Natural Resources in Hunan Province (grant number 2022-15), the Science and Technology Project of Hunan Provincial Natural Resources Department (grant number 2023JJ30582), and supported by Postgraduate Scientific Research Innovation Project of Hunan Province (grant number QL20220161) and Postgraduate Scientific Research Innovation Project of Xiangtan University(grant number XDCX2022L024).

Author information

Authors and Affiliations

  1. College of Automation and Electronic Information, Xiangtan University, Xiangtan, 411105, China

    Lijing Bu, Dong Dai, Zhengpeng Zhang, Xinyu Xie & Mingjun Deng

Authors
  1. Lijing Bu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengpeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinyu Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mingjun Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Dai .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bu, L., Dai, D., Zhang, Z., Xie, X., Deng, M. (2023). A Lightweight Hyperspectral Image Super-Resolution Method Based on Multiple Attention Mechanisms. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_53

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-4742-3_53

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation