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All issues Volume 39 / No 3 (June 2021) JNWPU, 39 3 (2021) 484-491 References
Open Access
Issue
JNWPU
Volume 39, Number 3, June 2021
Page(s) 484 - 491
DOI https://doi.org/10.1051/jnwpu/20213930484
Published online 09 August 2021
  1. Tong Qingxi, Zhang Bin, Zheng Lanfen. Hyperspectral remote sensing: the principle, technology and application[M]. Beijing: Higher Education Press, 2006 (in Chinese) [Google Scholar]
  2. Manolakis D, Shaw G. Detection algorithm for hyperspectral imaging applications[J]. IEEE Signal Processing Magazine, 2002, 19(1): 29–43 10.1109/79.974724 [NASA ADS] [CrossRef] [Google Scholar]
  3. Stein D, Beaven S, Hoff L, et al. Anomaly detection from hyperspectral imagery[J]. IEEE Signal Processing Magazine, 2002, 19(1): 58–69 10.1109/79.974730 [NASA ADS] [CrossRef] [Google Scholar]
  4. Matteoli S, Diani M, Corsini G. A tutorial overview of anomaly detection in hyperspectral images[J]. IEEE Aerospace and Electronic Systems Magazine, 2010, 25(7): 5–28 10.1109/MAES.2010.5546306 [CrossRef] [Google Scholar]
  5. Reed I, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Trans on Acoustics Speech and Signal Processing, 1990, 38(10): 1760–1770 10.1109/29.60107 [NASA ADS] [CrossRef] [Google Scholar]
  6. Li W, Du Q. Decision fusion for dual-window-based hyperspectral anomaly detector[J]. Journal of Applied Remote Sensing, 2015, 9(1): 097297–097297 10.1117/1.JRS.9.097297 [NASA ADS] [CrossRef] [Google Scholar]
  7. Chen S, Wang W, Wu C, et al. Real-time causal processing of anomaly detection for hyperspectral imagery[J]. IEEE Trans on Aerospace and Electronic Systems, 2014, 50: 1511–1534 10.1109/TAES.2014.130065 [NASA ADS] [CrossRef] [Google Scholar]
  8. Zhao C, Wang Y, Qi B, et al. Global and local real-time anomaly detectors for hyperspectral remote sensing imagery[J]. Remote Sensing, 2015, 7(4): 3966–3985 10.3390/rs70403966 [NASA ADS] [CrossRef] [Google Scholar]
  9. Kwon H, Nasrabadi N. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery[J]. IEEE Trans on Geoscience and Remote Sensing, 2005, 43(2): 388–397 10.1109/TGRS.2004.841487 [NASA ADS] [CrossRef] [Google Scholar]
  10. He M, Mei H, Wu Y, Yan H. Weighted kernel-based signature subspace projection for hyperspectral target detection[C]//9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2018: 1–5 [Google Scholar]
  11. Banerjee A, Burlina P, Diehl C. A support vector method for anomaly detection in hyperspectral imagery[J]. IEEE Trans on Geoscience and Remote Sensing, 2006, 44(8): 2282–2291 10.1109/TGRS.2006.873019 [NASA ADS] [CrossRef] [Google Scholar]
  12. Khazai S, Homayouni S, Safari A, et al. Anomaly detection in hyperspectral images based on an adaptive support vector method[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 646–650 10.1109/LGRS.2010.2098842 [NASA ADS] [CrossRef] [Google Scholar]
  13. Gurram P, Kwon H, Han T. Sparse kernel-based hyperspectral anomaly detection[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(5): 943–947 10.1109/LGRS.2012.2187040 [NASA ADS] [CrossRef] [Google Scholar]
  14. Cui X, Tian Y, Weng L, et al. Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition[C]//International Society for Optics and Photonics International Conference on Graphic and Image Processing, 2014: 9069 [Google Scholar]
  15. Zhang Y, Du B, Zhang L et al. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection[J]. IEEE Trans on Geoscience and Remote Sensing, 2016, 54(3): 1376–1389 10.1109/TGRS.2015.2479299 [NASA ADS] [CrossRef] [Google Scholar]
  16. Li W, Du Q. Collaborative representation for hyperspectral anomaly detection[J]. IEEE Trans on Geoscience and Remote Sensing, 2015, 53(3): 1463–1474 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  17. Li W, Wu G, Du Q. Transferred deep learning for anomaly detection in hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(5): 597–601 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  18. Efros A, Leung T, et al. Texture synthesis by non-parametric sampling[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999: 1033–1038 [Google Scholar]

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