EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 38 / No 4 (August 2020) JNWPU, 38 4 (2020) 740-746 References
Open Access
Issue
JNWPU
Volume 38, Number 4, August 2020
Page(s) 740 - 746
DOI https://doi.org/10.1051/jnwpu/20203840740
Published online 06 October 2020
  1. Heinle A, Macke A, Srivastav A. Automatic Cloud Classification of Whole Sky Images[J]. Atmospheric Measurement Techniques, 2010, 3(3): 557-567 10.5194/amt-3-557-2010 [CrossRef] [Google Scholar]
  2. GÓmez-Chova L, Camps-Valls G, Bruzzone L, et al. Mean Map Kernel Methods for Semisupervised Cloud Classification[J]. IEEE Trans on Geoscience and Remote Sensing, 2009, 48(1): 207-220 [Article] [CrossRef] [Google Scholar]
  3. Cazorla A, Olmo F J, Alados-Arboledas L. Development of a Sky Imager for Cloud Cover Assessment[J]. Journal of the Optical society of America A, 2008, 25(1): 29-39 10.1364/JOSAA.25.000029 [CrossRef] [Google Scholar]
  4. Rumi E, Kerr A D, Sandford B A, et al. Field Trial of an Automated Ground-Based Infrared Cloud Classification System[J]. Meteorological Applications, 2015, 22(4): 779-788 10.1002/met.1523 [CrossRef] [Google Scholar]
  5. Lee J, Weger R C, Sengupta S K, et al. A Neural Network Approach to Cloud Classification[J]. IEEE Trans on Geoscience and Remote Sensing, 1990, 28(5): 846-855 10.1109/36.58972 [CrossRef] [Google Scholar]
  6. Ambroise C, Sèze G, Badran F, et al. Hierarchical Clustering of Self-Organizing Maps for Cloud Classification[J]. Neurocomputing, 2000, 30(1/2/3/4): 47-52 [Article] [CrossRef] [Google Scholar]
  7. Lee Y, Wahba G, Ackerman S A. Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines[J]. Journal of Atmospheric and Oceanic Technology, 2004, 21(2): 159-169 10.1175/1520-0426(2004)021<0159:CCOSRD>2.0.CO;2 [CrossRef] [Google Scholar]
  8. Singh M, Glennen M. Automated Ground-Based Cloud Recognition[J]. Pattern Analysis and applications, 2005, 8(3): 258-271 10.1007/s10044-005-0007-5 [CrossRef] [Google Scholar]
  9. Kazantzidis A, Tzoumanikas P, Bais F, et al. Cloud Detection and Classification with the Use of Whole-Sky Ground-Based Images[J]. Atmospheric Research, 2012, 113:80-88 10.1016/j.atmosres.2012.05.005 [CrossRef] [Google Scholar]
  10. Liu S, Wang C, Xiao B, et al. Salient Local Binary Pattern for Ground-Based Cloud Classification[J]. Acta Meteorologica Sinica, 2013, 27(2): 211-220 10.1007/s13351-013-0206-8 [CrossRef] [Google Scholar]
  11. Zhuo W, Cao Z, Xiao Y. Cloud Classification of Ground-Based Images Using Texture-Structure Features[J]. Journal of Atmospheric and Oceanic Technology, 2014, 31(1): 79-92 10.1175/JTECH-D-13-00048.1 [CrossRef] [Google Scholar]
  12. Taravat A, Del Frate F, Cornaro C, et al. Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3): 666-670 10.1109/LGRS.2014.2356616 [CrossRef] [Google Scholar]
  13. Zhang Z, Zhang Y, Liu S. Ground-Based Cloud Classification Using Pyramid Salient LBP[C]//Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, Heidelberg, 2016 [Google Scholar]
  14. Liu S, Zhang Z, Cao X. Information Integration for Ground-Based Cloud Classification Using Joint Consistent Sparse Coding in Heterogeneous Sensor Network[J]. Signal Processing, 2016, 126: 165-172 10.1016/j.sigpro.2015.06.004 [CrossRef] [Google Scholar]
  15. Gan J, Lu W, Li Q, et al. Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 17: 3360-3372 [Article] [CrossRef] [Google Scholar]
  16. Wang Y, Shi C, Wang C, et al. Ground-based Cloud Classification by Learning Stable Local Binary Patterns[J]. Atmospheric Research, 2018, 207: 74-89 10.1016/j.atmosres.2018.02.023 [CrossRef] [Google Scholar]
  17. Di Xiaohong, Wang Xiaonan, Xiao Wei, et al. Cloud Features Classification of Short-Time Heavy Rainfall in Complex Topography of Plateau Slope[J]. Meteorological Monthly, 2018, 44(11): 1445-1453 [Article] (in Chinese) [Google Scholar]
  18. Pan S J, Yang Q. A Survey on Transfer Learning[J]. IEEE Trans on knowledge and Data engineering, 2009, 22(10): 1345-1359 [Article] [Google Scholar]
  19. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//International Conference on Learning Representations, 2015 [Google Scholar]
  20. Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015 [Google Scholar]
  21. He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.