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Superresolution Approach of Remote Sensing Images based on Deep Convolutional Neural Network

Volume 14, Number 3, March 2018, pp. 463-472
DOI: 10.23940/ijpe.18.03.p7.463472

Jitao Zhanga, Aili Wanga, Na Anb, and Yuji Iwahoric

aHigher Education Key Lab for Measure& Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, 150080, China
bHytera Communications Corporation Limited, Harbin, 150001, China
cDepartment of Computer Science, Chubu University, Aichi, Japan

(Submitted on December 6, 2017; Revised on January 16, 2018; Accepted on February 12, 2018)


Abstract:

Nowadays, remote sensing images have been widely used in civil and military fields. But, because of the limitations of the current imaging sensors and complex atmospheric conditions, the resolution of remote sensing images is often low. In this paper, a superresolution reconstruction algorithm based on the deep convolution neural network to improve the resolution of the remote sensing image is proposed. First, this algorithm learned a series of features of the mapping between high and low resolution images in the training phase. This mapping is expressed as a kind of deep convolutional neural network; the trained network is a series of parameter optimization for super-resolution reconstruction of remote sensing image. Experimental results show that the superresolution algorithm proposed in this paper can keep the details subjectively and improve the evaluation index objectively.

 

References: 18

  1. Z. Cui, H. Chang and S. Shan, “Deep Network Cascade for Image Super-resolution,” Computer Vision – ECCV 2014. Springer International Publishing, pp.49-64, 2014.
  2. C. Dong, L. C. Chen and K. He, “Image Super-Resolution Using Deep Convolutional Networks,” IEEE Transactions on Pattern Analysis & Machine Intelligence, 38(2), pp.295-307, 2016.
  3. A. Devi, T. Geetha, Madhum and K. Lal Kishore. : “A Novel Super Resolution Algorithm based on Fuzzy Bicubic Interpolation Algorithm,” International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(8), pp.283-298, 2015.
  4. A. Devi, T. Geetha, Madhum and K. Lal Kishore. : “An improved super resolution image reconstruction using SVD based fusion and blind deconvolution techniques.” International Journal of Signal Processing, Image Processing and Pattern Recognition, 7(1), pp.283-298, 2014.
  5. L. Liebel and M. Körner, “Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks,” ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.883-890, 2016.
  6. Q. Luo, X. Shao and L. Wang, “Super-resolution imaging in remote sensing,” Proceedings of SPIE - The International Society for Optical Engineering, 9501(1), pp.175–181, 2015.
  7. S. Liu, Y. Zhu and L. Xue, “Remote sensing image super-resolution method using sparse representation and classified texture patches,” Wuhan Daxue Xuebao, 40(5), pp.578-582, 2015.
  8. Tang and Ling, “Blind Super-Resolution Image Reconstruction Based on Weighted POCS,” International Journal of Multimedia & Ubiquitous Engineering, 11(5), pp.367-376, 2016.
  9. H. Tao, X. Tang and J. Liu, “Super-resolution remote sensing image processing algorithm based on wavelet transform and interpolation,” Proceedings of SPIE-The International Society for Optical Engineering, 4898, pp.259-263, 2002.
  10. R. Timofte, D. V. Smet and V. L. Gool, “A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution,” Computer Vision -- ACCV 2014, pp.111-126, 2015.
  11. W. Wang, H. Li, X. Zhang: “Fusion Algorithm of Remote Sensing Images Based on Nonsubsampled Pyramid and Empirical Mode of Demoposition,” Journal of Harbin Engineering, 11, pp.1394-1398, 2012.
  12. W. Wu, X. Yang and K. Liu, “A new framework for remote sensing image super-resolution: Sparse representation-based method by processing dictionaries with multi-type features,” Journal of Systems Architecture, 64, pp.63-75, 2016.
  13. W. Wu, X. Lu, Yang and K. Liu, “A new framework for remote sensing image super-resolution: Sparse representation-based method by processing dictionaries with multi-type features,” Journal of Systems Architecture, 67, pp.105-117, 2016.
  14. Z. Wang, Y. Yang and Z. Wang, “Learning Super-Resolution Jointly from External and Internal Examples,” IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 24(11), pp.4359-4371, 2015.
  15. C. J. Yang, J. Wright and T. Huang, “Image super-resolution via sparse representation,” IEEE Transaction on Image Processing. 19(11), pp.2861-2873, 2010.
  16. X. Yang, W. Wu and W. Chen, “Remote Sensing Image Super-resolution Using Dual-Dictionary Pairs Based on Sparse Presentation and Multiple Features,” Proceedings of International Conference on Internet Multimedia Computing and Service. ACM, pp.90-94, 2014.
  17. Y. Zhao, J. Yang, W. C. Chan, “Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity,” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 7(6), pp.2671-2679, 2014.
  18. Y. Zhang, W. Wu, Y. Dai, “Remote Sensing Images Super-resolution Based on Sparse Dictionaries and Residual Dictionaries,” IEEE International Conference on Dependable, Autonomic and Secure Computing, pp.318-323, 2013.

 

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