Open Access
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
Article Number 03046
Number of page(s) 5
Section Parallel Session II: Water System Technology
Published online 07 December 2018
  1. S. Yu, C. Xu, and S. Jia, “Convolutional neural networks for hyperspectral image classification,” Neurocomputing, 219: 88–98, (2017) [CrossRef] [Google Scholar]
  2. T. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, Y. Ma, “PCANet: A Simple Deep Learning Baseline for Image Classification?,” IEEE Transactions on Image Processing, vol. 24. pp: 5017-5032, (2015) [CrossRef] [Google Scholar]
  3. R. Liu, and T. Lu, “Character Recognition Based on PCANet,” 2016 15th International Symposium on Parallel and Distributed Computing(ISPDC), pp: 364-367, (2016) [CrossRef] [Google Scholar]
  4. C. Chen, D. Wang, and H. Wang, “Scene Character Recognition Using PCANet,” ICIMCS 2015: 65:1-65:4, (2015) [Google Scholar]
  5. F. Melgani, and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, (2004) [NASA ADS] [CrossRef] [Google Scholar]
  6. M. Chi, and L. Bruzzone, “Semisupervised classification of hyperspectral images by SVMs optimized in the primal,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1870-1880, (2007) [CrossRef] [Google Scholar]
  7. J. Li, J. M. Bioucas-Dias, and A. Plaza, “Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 2, pp. 318-322, (2013) [CrossRef] [Google Scholar]
  8. B. Pan, Z. Shi, and X. Xu, “MugNet: Deep learning for hyperspectral image classification using limited samples,” ISPRS, vol. 145, Part A, pp. 108-119, (2018) [CrossRef] [Google Scholar]
  9. F. Wang, R. Zhang, and Q. Wu, “Hyperspectral image classification based on PCA network”, IEEE 2016 8th WHISPERS, pp: 1-4, (2016) [Google Scholar]
  10. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Advances in spectralspatial classification of hyperspectral images,” Proceedings of the IEEE, vol. 101, no. 3, pp. 652-675, (2013) [CrossRef] [Google Scholar]
  11. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun, “What is the best multi-stage architecture for object recognition,” in ICCV, (2009) [Google Scholar]
  12. A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural network,” in NIPS, (2012) [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.