Open Access
Issue
MATEC Web of Conferences
Volume 45, 2016
2016 7th International Conference on Mechatronics and Manufacturing (ICMM 2016)
Article Number 05001
Number of page(s) 6
Section Computer aided manufacturing technology
DOI https://doi.org/10.1051/matecconf/20164505001
Published online 15 March 2016
  1. B. B. Marschallinger, D. Sabel, W. Wagner, “Optimisation of global grids for high-resolution remote sensing data,” Computers & Geosciences, vol. 72, pp. 84–93, November 2014. [CrossRef]
  2. K. L. Wagstaff, D. R. Thompson and W. K. Abbey, “texture-sensitive instrument classification for in situ rock and layer analysis,” Geophysical Research Letters, vol. 40, no. 16, pp. 4188–4193, August 2013. [CrossRef]
  3. S. Lucana, M. Enrico, V. Raffaele, “Highly-Parallel GPU Architecture for Lossy Hyperspectral Image Compression,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS), vol. 6, no. 2, pp. 670–681, April 2013. [CrossRef]
  4. S. Bernabe, S. Lopez, A. Plaza, “FPGA Design of an Automatic Target Generation Process for Hyperspectral Image Analysis,” in IEEE 17th International Conference on Parallel and Distributed Systems, 2011, pp. 1010–1015.
  5. G. Camps Valls, L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” in IEEE Transaction on Geoscience and Remote Sensing, vol. 43, no. 6, pp. 1351–1362, 2005. [CrossRef]
  6. H. Guo, W. Wang, “An active learning-based SVM multi-class classification model,” in Pattern Recognition, vol. 48, no. 5, pp. 1577–1597, May 2015. [CrossRef]
  7. W. H. Chih, J. L. Chih, “A comparison of methods for multiclass support vector machines,” in IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, March 2002. [CrossRef]
  8. S. Hari, F. Agrafioti and D. Hatzinakos, “Design of a Hamming-distance Classifier for ECG-Biometrics,” in Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 3009–3012.
  9. A. H. M. Jallad and L. B. Mohammed, “Hardware Support Vector Machine (SVM) for satellite on-board applications,” in Adaptive Hardware and Systems (AHS), 2014, pp. 256–261.
  10. J. Manikandan, B. Venkataramani, “System-on-programmable -chip implementation of diminishing learning based pattern recognition system,” in International Journal of Machine Learning and Cybernetics, vol. 4, no. 4, pp. 347–363, Augst 2013. [CrossRef]
  11. J. Suykens and J. Vandewalle, “Least square support vector machine classifiers,” in Neural Processing Letters, vol. 9, no. 3, pp. 293–300, March 1999. [CrossRef]
  12. A. Vempaty, L. R. Varshney, P. K. Varshney, “Reliable Crowdsourcing for Multi-Class Labeling Using Coding Theory,” in IEEE Transactions on Signal Processing, vol. 8, no. 4, pp. 667–679, AUGUST 2014.
  13. R. Green, M. Eastwood, M. Sarture, “Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” in Remote Sensing of Environment, vol. 6, no. 3, pp. 227–248, September, 1998. [CrossRef]
  14. Computational Intelligence Group, Hyperspectral Remote Sensing Scenes datasets, 2015 [Online]. Available: http://alweb.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes.
  15. Q. Sami ul Haq, L. Tao, S. Yang, “Neural Network based Adaboosting Approach for Hyperspectral Data Classification,” in International Conference on Computer Science and Network Technology, 2011, pp. 241–245.
  16. P. H. Hsu, H. H. Yang, “Hyperspectral Image Classification Using Wavelet Networks,” in International Conference on Geoscience and Remote Sensing Symposium, 2007, pp. 1767–1770.
  17. M. Khodadadzadeh, J. Li, A. Plaza, P. Gamba, “A New Framework for Hyperspectral Image Classification Using Multiple Spectral and Spatial Features,” in International Conference on Geoscience and Remote Sensing Symposium, 2014, pp. 4628–4631.
  18. Z. H. Xue, P. J. Du, H. J. Su, “Harmonic Analysis for Hyperspectral Image Classification Integrated With PSO Optimized SVM,” in International Conference on Computer Science and Network Technology, 2011, pp. 241–245.
  19. Q. Sami ul Haq, L. Tao, S. Yang, “Neural Network based Adaboosting Approach for Hyperspectral Data Classification,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2131–2146, 2014. [CrossRef]
  20. Jet Propulsion Laboratory, AVIRIS Instrument, 2015 [Online]. Available: http://aviris.jpl.nasa.gov/aviris/instrument.html.

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.