Deep learning for ocean remote sensing: An application of convolutional neural networks for super-resolution on satellite-derived SST data

2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2016

Volumn , Issue , 2017, Pages

  Ducournau, Aurelien ^a   Fablet, Ronan ^a  

^a INSTITUT MINES TELECOM   (France)

Author keywords

Convolutional Neural Networks; Deep Learning; Ocean Remote Sensing Data; Super Resolution

Indexed keywords

ATMOSPHERIC TEMPERATURE; CONVOLUTION; DEEP LEARNING; NETWORK ARCHITECTURE; NEURAL NETWORKS; OCEANOGRAPHY; OPTICAL RESOLVING POWER; PATTERN RECOGNITION; SATELLITES; SURFACE WATERS;

CONVOLUTIONAL NEURAL NETWORK; IMAGE SUPER RESOLUTIONS; LARGE-SCALE DATASET; LEARNING ARCHITECTURES; OCEAN REMOTE SENSING; REMOTE SENSING DATA; SEA SURFACE TEMPERATURE (SST); SUPER RESOLUTION;

REMOTE SENSING;

EID: 85016967048     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PRRS.2016.7867019     Document Type: Conference Paper

References (24)

1. K. Goubanova, V. Echevin, B. Dewitte, F. Codron, K. Takahashi, P. Terray, M. Vrac, "Statistical downscaling of sea-surface wind over the Peru-Chile upwelling region: Diagnosing the impact of climate change from the IPSL-CM4 model, " Climate Dynamics, vol. 36, no. 7-8, pp. 1365-1378, May 2010.
2. L. He, R. Fablet, B. Chapron, J. Tournadre, "Learning-Based Emulation of Sea Surface Wind Fields From Numerical Model Outputs and SAR Data, " IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 10, pp. 4742-4750, Oct. 2015.
3. J. Brajard, C. Moulin, S. Thiria, "Atmospheric correction of SeaWiFS ocean color imagery in the presence of absorbing aerosols off the Indian coast using a neuro-variational method, " Geophysical Research Letters, vol. 35, p. 20604, Oct. 2008.
4. A. Alvera Azcarate, A. Barth, D. Sirjacobs, F. Lenartz, J.-M. Beckers, "Data Interpolating Empirical Orthogonal Functions (DINEOF): A tool for geophysical data analyses, " Mediterranean Marine Science, vol. 12, no. 3, 2011.
5. R. Fablet and F. Rousseau, "Joint Interpolation of Multisensor Sea Surface Temperature Fields Using Nonlocal and Statistical Priors, " IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2665-2675, Jun. 2016.
6. C.-Y. Yang, C. Ma, M.-H. Yang, "Single-Image Super-Resolution: A Benchmark, " in Computer Vision ECCV 2014, ser. Lecture Notes in Computer Science. Springer International Publishing, Sep. 2014, no. 8692, pp. 372-386.
7. R. Timofte, R. Rothe, L. Van Gool, "Seven ways to improve example-based single image super resolution, " in 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, arXiv: 1511. 02228.
8. K. I. Kim and Y. Kwon, "Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, Jun. 2010.
9. J. Yang, Z. Lin, S. Cohen, "Fast Image Super-Resolution Based on In-Place Example Regression, " in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2013, pp. 1059-1066.
10. J. B. Huang, A. Singh, N. Ahuja, "Single image super-resolution from transformed self-exemplars, " in 2015 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2015, pp. 5197-5206.
11. H. Chang, D.-Y. Yeung, Y. Xiong, "Super-resolution through neighbor embedding, " in 2004 IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, Jun. 2004, pp. I-I.
12. S. Schulter, C. Leistner, H. Bischof, "Fast and accurate image upscaling with super-resolution forests, " in 2015 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2015, pp. 3791-3799.
13. J. Yang, J. Wright, T. S. Huang, Y. Ma, "Image Super-Resolution Via Sparse Representation, " IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2861-2873, Nov. 2010.
14. R. Timofte, V. DeSmet, L. VanGool, "A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, " in Computer Vision-ACCV 2014, ser. Lecture Notes in Computer Science. Springer International Publishing, Nov. 2014, no. 9006, pp. 111-126.
15. S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, L. Zhang, "Convolutional Sparse Coding for Image Super-Resolution, " in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1823-1831.
16. C. Dong, C. C. Loy, K. He, X. Tang, "Image Super-Resolution Using Deep Convolutional Networks, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295-307, Feb. 2016.
17. J. Kim, J. K. Lee, K. M. Lee, "Accurate Image Super-Resolution Using Very Deep Convolutional Networks, " arXiv:1511. 04587 [cs], Nov. 2015, arXiv: 1511. 04587.
18. -, "Deeply-Recursive Convolutional Network for Image Super-Resolution, " arXiv:1511. 04491 [cs], Nov. 2015, arXiv: 1511. 04491.
19. J. Xie, L. Xu, E. Chen, "Image Denoising and Inpainting with Deep Neural Networks, " in Advances in Neural Information Processing Systems, 2012, pp. 350-358.
20. L. A. Gatys, A. S. Ecker, M. Bethge, "Texture Synthesis Using Convolutional Neural Networks, " in Advances in Neural Information Processing Systems 28, 2015.
21. C. J. Donlon, M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, W. Wimmer, "The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, " Remote Sensing of Environment, vol. 116, pp. 140-158, Jan. 2012.
22. Y. LeCun, Y. Bengio, G. Hinton, "Deep learning, " Nature, vol. 521, no. 7553, pp. 436-444, May 2015.
23. J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, L. Fei-Fei, "ImageNet: A large-scale hierarchical image database, " in IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2009, pp. 248-255.
24. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, "Caffe: Convolutional Architecture for Fast Feature Embedding. " ACM Press, 2014, pp. 675-678.