Open Access
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
Article Number 03071
Number of page(s) 4
Section Digital Signal and Image Processing
Published online 19 June 2018
  1. C. C. Chang and C. J. Lin, LIBSVM, A library for support vector machines, (2001) [Google Scholar]
  2. Ingram, R. N., et al., International Journal of Remote Sensing 25(22), An automatic nonlinear correlation approach for processing of Hyper-spectral images. 4981-4998. (2004) [Google Scholar]
  3. Du, Q., et al., IEEE Geoscience and Remote Sensing Letters 6(4), Segmented Principal Component Analysis for Parallel Compression of Hyper-spectral imagery. 713-717. (2009) [Google Scholar]
  4. Plaza, A., et al., Remote Sensing of Environment 113, Recent advances in techniques for Hyper-spectral image processing. S110-S122. (2009) [Google Scholar]
  5. Plaza, A., et al., Ieee Signal Processing Magazine 28(3), Parallel Hyper-spectral image and Signal Processing. (2011) [Google Scholar]
  6. Lena C., et al., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4(3), Group and region based parallel compression method using signal subspace projection and band clustering for hyperspectral imagery. 565-578. (2011) [Google Scholar]
  7. Lucana S., et al., Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 4th Workshop on Hyperspectral Image and Signal Processing, GPU implementation of a lossy compression algorithm for hyperspectral images. (2012) [Google Scholar]
  8. Zhao, X. and X. Qiao, Spectral Characteristics Research of the Hyperspectral Image Based on the Correlation Matrix. Information Science and Engineering (ISISE), 2012 International Symposium on. (2012) [Google Scholar]
  9. Santos, L., et al., Journal of Applied Remote Sensing 7, Lossy Hyper-spectral image compression on a graphics processing unit: parallelization strategy and performance evaluation. (2013) [Google Scholar]
  10. Taher, A., et al., Hyperspectral image segmentation using a cooperative nonparametric approach. Image and Signal Processing for Remote Sensing Xix. L. Bruzzone. 8892. (2013) [Google Scholar]
  11. Santos, L., et al., Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(2), Highly-Parallel GPU Architecture for Lossy Hyper-spectral image Compression. 670-681. (2013) [Google Scholar]
  12. Sanchez, S., et al. PARALLEL HYPER-SPECTRAL IMAGE COMPRESSION USING ITERATIVE ERROR ANALYSIS ON GRAPHICS PROCESSING UNITS. 2012 Ieee International Geoscience and Remote Sensing Symposium: 3474-3477. (2012) [CrossRef] [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.