189
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A CNN-based rescaling algorithm and performance analysis for spatial resolution enhancement of Landsat images

, , , &
Pages 607-629 | Received 27 Jun 2021, Accepted 27 Dec 2021, Published online: 29 Jan 2022

References

  • Aburaed, N., A. Panthakkan, S. Almansoori, and H. Al-Ahmad. 2019. ”Super Resolution of DS-2 Satellite Imagery Using Deep Convolution Neural Network.” In Image and Signal Processing for Remote Sensing XXV, edited by L. Bruzzone; E. Bovolo, and J. A. Benediktsson. Strasbourg, France: Conference on Image and Signal Processing for Remote Sensing XXV. Sep 0911. 11155, UNSP 111551I. 0911. 11155, UNSP 111551I.
  • Cao, K. R., Y. Q. Liu, L. N. Duan, and T. Xie. 2020. “Adaptive Residual Channel Attention Network for Single Image Super-Resolution.” Scientific Programming 2020: 8877851. doi:https://doi.org/10.1155/2020/8877851.
  • Collins, C. B., J. M. Beck, S. M. Bridges, J. A. Rushing, and S. J. Graves. 2017. “Deep Learning for Multisensor Image Resolution Enhancement.” In Proceedings of the 1st ACM SIGSPATIAL Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery. Vol. 11, 37–44, November 7–10. Los Angeles, CA: GeoAI’17.
  • Dong, C., C. C. Loy, K. He, and X. Tang. 2014. “Learning a Deep Convolutional Network for Image Super-Resolution.” In the 13th European Conference on Computer Vision. Vol. 9, 184–199, Zurich, Switzerland, September 6–12.
  • Ha, J., Y. Kim, and J. Kim. 2020. “Single Image Super-Resolution via Similarity Between Spatially Scattered Features.” IEEE Access 8: 137672–137682. doi:https://doi.org/10.1109/ACCESS.2020.3011566.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, 770–778, Las Vegas, NV, June 27–30.
  • Hu, W., Y. Li, W. Zhang, S. Chen, X. Lv, and L. Ligthart. 2019. “Spatial Resolution Enhancement of Satellite Microwave Radiometer Data with Deep Residual Convolutional Neural Network.” Remote Sensing 11 (7): 771. doi:https://doi.org/10.3390/rs11070771.
  • Huang, J. B., A. Singh, and N. L. Ahuja. 2015. “Single Image Super-Resolution from Transformed Self-Exemplars.” In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 5197–5206, Boston, MA, June 7–12.
  • Jensen, J. R. 2016. Introductory Digital Image Processing: A Remote Sensing Perspective. 4th ed. Glenview IL, USA: Pearson Prentice Hall.
  • Kim, J., J. Kwon Lee, and K. M. Lee. 2016. “Accurate Image Super-Resolution Using Very Deep Convolutional Networks.” In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 1646–1654, Las Vegas, NV, June 27-30.
  • Ledig, C., L. Theis, F. Huszã¡r, J. Caballero, A. Cunningham, and A. Acosta et al. 2016. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” In Proceedings of the 2017 IEEE conference on computer vision and pattern recognition, 1637–1645, Honolulu, HI, July 21-26.
  • Li, K., S. H. Yang, R. T. Dong, X. Y. Wang, and J. Q. Huang. 2020. “Survey of Single Image Super-Resolution Reconstruction.” IET Image Processing 14 (11): 2273–2290. doi:https://doi.org/10.1049/iet-ipr.2019.1438.
  • Li, Z., Q. L. Li, W. Wu, J. L. Yang, Z. Y. Li, and X. M. Yang. 2020. “Deep Recursive Up-Down Sampling Networks for Single Image Super-Resolution.” Neurocomputing 398: 377–388. doi:https://doi.org/10.1016/j.neucom.2019.04.004.
  • Madhukar, B. N. and R. Narendra. 2013. ”Lanczos Resampling for the Digital Processing of Remotely Sensed Images.” In Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013), edited by V. S. Chakravarthi; Y. J. M. Shirur, and P. Prasad, 403–411. India, Springer.
  • Mao, X., C. Shen, and Y. B. Yang. 2016. “Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections.” In 30th Conference on Neural Information Processing Systems (NIPS 2016), 2802–2810, Barcelona, Spain.
  • Pashaei, M., M. J. Starek, H. Kamangir, and J. Berryhill. 2020. “Deep Learning-Based Single Image Super-Resolution: An Investigation for Dense Scene Reconstruction with UAS Photogrammetry.” Remote Sensing 12 (11): 1757. doi:https://doi.org/10.3390/rs12111757.
  • Pouliot, D., R. Latifovic, J. Pasher, and J. Duffe. 2018. “Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training.” Remote Sensing 10 (3): 394. doi:https://doi.org/10.3390/rs10030394.
  • Qin, M., S. Mavromatis, L. Hu, F. Zhang, R. Liu, J. Sequeira, and Z. Du. 2020. “Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement.” Remote Sensing 12 (5): 758. doi:https://doi.org/10.3390/rs12050758.
  • Schulter, S., C. Leistner, and H. Bischof. 2015. “Fast and Accurate Image Upscaling with Super-Resolution Forests.” In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Vol. 9, 3791–3799, Boston, MA, June 7–12.
  • Simonyan, K. and A. Zisserman. 2020. “Very Deep Convolution Networks for Large-Scale Image Recognition.” Accessed 20 August 2020. https://arxiv.org/pdf/1409.1556.pdf
  • Szegedy, C., W. Liu, Y. Jia, P. Sermanet, and S. Reed, et al. 2015. “Going Deeper with Convolutions.” In Proceedings of the 2015 IEEE conference on computer vision and pattern recognition, 1–9, Boston, MA, June 7–12.
  • Tai, Y., J. Yang, and X. Liu. 2017. “Image Super-Resolution via Deep Recursive Residual Network.” In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 1–9, Honolulu, HI, July 21–26.
  • Tang, Y. L., J. S. Huang, F. E. Zhang, and W. G. Gong. 2020. “Deep Residual Networks with a Fully Connected Reconstruction Layer for Single Image Super-Resolution.” Neurocomputing 405: 186–199. doi:https://doi.org/10.1016/j.neucom.2020.04.030.
  • Timofte, R., V. De Smet, and L. A. Van Gool. 2014. “Adjusted Anchored Neighborhood Regression for Fast Super-Resolution.” In Asian Conference on Computer Vision. Vol. 11, 111–126, Singapore, November 1–5.
  • Wang, G., G. Z. Gertner, P. Parysow, and A. B. Anderson. 2001. “Spatial Prediction and Uncertainty Assessment of Topographic Factor for Revised Universal Soil Loss Equation Using Digital Elevation Models.” ISPRS Journal of Photogrammetry and Remote Sensing 56 (1): 65–80. doi:https://doi.org/10.1016/S0924-2716(01)00035-1.
  • Wang, W., Y. H. Hu, Y. H. Luo, and T. Zhang. 2020. “Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches.” Sensing and Imaging 21 (1): 21. doi:https://doi.org/10.1007/s11220-020-00285-4.
  • Welstead, S. T. 1999. Fractal and Wavelet Image Compression Techniques. Bellingham, USA: SPIE Publication.
  • Yamanaka, J., S. Kuwashima, and T. Kurita. 2017. “Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network.” In the Proceedings of the 24thInternational Conference of Neural Information Processing. Vol. 11, 217–225, Guangzhou, China, November 14-18.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.