Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 02054
Number of page(s) 4
Section 3D Images Reconstruction and Virtual System
Published online 19 November 2018
  1. Reed I.S., Yu X., Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution, IEEE Transactions on Acoustics, Speech and Signal Processing, 38, 10(1990). [Google Scholar]
  2. Borghys D., Kåsen I., Achard V., et al, Comparative evaluation of hyperspectral anomaly detectors in different types of background. Algorithms & Technologies for Multispectral Hyperspectral & Ultraspectral Imagery, International Society for Optics and Photonics, San Diego, 12(2012). [Google Scholar]
  3. A.P. Schaum, Hyperspectral anomaly detection beyond RX, Proc. SPIE, 2007. [Google Scholar]
  4. Kwon H., Nasrabad N.M., Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 43,2(2005). [CrossRef] [Google Scholar]
  5. Kwon H., Nasrabadi N.M., A comparative study of kernel spectral matched signal detectors for hyper-spectral target detection, USA:SPIE The International Society for Optical Engineering, 2005. [Google Scholar]
  6. Yuan Zongze, Sun Hao, Ji Kefeng, et al, Local Sparsity Divergence for Hyperspectral Anomaly Detection, IEEE Geoscience and Remote Sensing Letters, 11, 10(2014). [CrossRef] [Google Scholar]
  7. Zhang Lili, Zhao Chunhui, Cheng Baozhi, A joint kernel collaborative representation based approach for hyperspectral image anomaly target detection, Journal of Optoelectronics·laser, 11(2015). [Google Scholar]
  8. Lili Zhang, Baozhi Cheng, Yuwei Deng, A tensor-based adaptive subspace detector for hyperspectral anomaly detection, International Journal of Remote Sensing, 39, 8(2018). [Google Scholar]
  9. Zhang Hanling, Application of MATLAB in image processing, Tsinghua University Press, 2008. [Google Scholar]
  10. Song Xiangfa, Jiao Licheng, Hyperspectral remote sensing image based on sparse representation and spectral Information, Journal of Electronics & Information Technology, 34, 2(2012). [Google Scholar]
  11. Zhao Chunhui, Jing Xiaohao, Li Wei, Hyperspectral imagery target detection algorithm based on StOMP sparse representation, Journal of Harbin Engineering University, 36, 7(2015). [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.