Skip to main content
SpringerLink
Log in

Multi-scale Deep Residual Network for Satellite Image Super-Resolution Reconstruction

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11859))

Included in the following conference series:

Abstract

Satellite images are used in all aspects of human life, and the demand for high-resolution satellite images is increasing dramatically as human technology advances. The most straightforward way to improve imaging resolution is to improve hardware design or reduce satellite flight altitude, but at a higher cost and with unbreakable physical limits. Super-resolution reconstruction is a way to improve image resolution. Satellite imagery has a wide imaging range. The scale of the ground target varies greatly and the texture information is diversified, which brings new challenges to the existing image super-resolution technology. A multi-scale residual deep neural network is proposed for the multi-scale characteristics of satellite imagery in this paper. In the middle of the residual body, the series-parallel combined dilated convolution is used to obtain different sizes of receptive fields which can achieve different scale information, and finally generate high-resolution satellite images after pixel shuffle. The experimental results on the Airbus satellite ship image dataset prove the superiority of the proposed algorithm.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Sharon, P., Yehezkel, Y.: Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn. Reson. Med. 45, 29–35 (2001)

    Article  Google Scholar 

  2. Matt, W., Peter, M., Holland, D.A.: Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel-swapping. Int. J. Remote Sens. 27, 473–491 (2006)

    Article  Google Scholar 

  3. Shermeyer, J., Van Etten, A.: The effects of super-resolution on object detection performance in satellite imagery. arXiv preprint arXiv:1812.04098 (2018)

  4. Luo, Y., Zhou, L., Wang, S.: Video satellite imagery super resolution via convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 14(12), 2398–2402 (2017)

    Article  Google Scholar 

  5. Bee, L.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)

    Google Scholar 

  6. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

  7. Liu, D.: Robust single image super-resolution via deep networks with sparse prior. IEEE Trans. Image Process. 25, 3194–3207 (2016)

    Article  MathSciNet  Google Scholar 

  8. Fan, Y.: Balanced two-stage residual networks for image super-resolution. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1157–1164 (2017)

    Google Scholar 

  9. Tai, Y.: Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4539–4547 (2017)

    Google Scholar 

  10. Tong, T.: Image super-resolution using dense skip connections. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4809–4817 (2017)

    Google Scholar 

  11. Zhang, Y.: Residual dense network for image super-resolution. arXiv e-prints arXiv:1802.08797 (2018)

  12. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  13. Tsai, R.Y., Huang, T.S.: Multiple frame image restoration and registration. In: Advances in Computer Vision and Image Processing, Greenwich, pp. 317–339 (1984)

    Google Scholar 

  14. Batz, M., Eichenseer, A., Seiler, J.: Hybrid super-resolution combining example-based single-image and interpolation-based multi-image reconstruction approaches. In: IEEE International Conference on Image Processing (ICIP), pp. 58–62 (2015)

    Google Scholar 

  15. Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127–1133 (2010)

    Article  Google Scholar 

  16. Lian, Q.S., Zhang, W.: Super-resolution reconstruction algorithm based on classification of sparse representations of image blocks. J. Electron. 40(5), 920–925 (2012)

    Google Scholar 

  17. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

    Chapter  Google Scholar 

  18. Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25

    Chapter  Google Scholar 

  19. Shi, W., Caballero, J., Huszár, F.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR (2016)

    Google Scholar 

  20. Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: CVPR (2016)

    Google Scholar 

  21. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: IEEE Computer Vision and Pattern Recognition (CVPR). IEEE (2017)

    Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2015)

    Google Scholar 

  23. Lim, B., Son, S., Kim, H.: Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society (2017)

    Google Scholar 

  24. Mao, X.J., Shen, C., Yang, Y.B.: Image restoration using convolutional auto-encoders with symmetric skip connections. In: NIPS (2016)

    Google Scholar 

  25. Fisher, Y., Vladlen, K.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

  26. Zhao, H.S., Shi, J.P., Qi, X.J., Wang, X.G., Jia, J.Y.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)

    Google Scholar 

  27. Fisher, Y., Vladlen, K., Thomas, F.: Dilated residual networks. In: Computer Vision and Pattern Recognition, vol. 1 (2017)

    Google Scholar 

  28. Zhou, L., Zhang, C., Wu, M.: D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2018)

    Google Scholar 

  29. Yu, J., Fan, Y., Yang, J., et al.: Wide activation for efficient and accurate image super-resolution. In: CVPR (2018)

    Google Scholar 

  30. https://www.kaggle.com/c/airbus-ship-detection/data

  31. Adam, P., et al.: Automatic differentiation in Pytorch (2017)

    Google Scholar 

  32. Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16817-3_8

    Chapter  Google Scholar 

  33. Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, pp. 5835–5843 (2017)

    Google Scholar 

  34. Xu, W.J., Xu, G.L., Wang, Y., Sun, X., Lin, D.Y., Wu, Y.W.: Deep memory connected neural network for optical remote sensing image restoration. Remote Sens. 10, 1893 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Wen Xu, Chuang Zhang & Ming Wu

Authors
  1. Wen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuang Zhang .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi’an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi’an, China

    Yanning Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, W., Zhang, C., Wu, M. (2019). Multi-scale Deep Residual Network for Satellite Image Super-Resolution Reconstruction. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31726-3_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31725-6

  • Online ISBN: 978-3-030-31726-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation