• Journal of Infrared and Millimeter Waves
  • Vol. 34, Issue 1, 114 (2015)
ZHAO Chun-Hui1、*, WANG Yu-Lei1、2, and LI Xiao-Hui1、3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3724/sp.j.1010.2015.00114 Cite this Article
    ZHAO Chun-Hui, WANG Yu-Lei, LI Xiao-Hui. A real-time anomaly detection algorithm for hyperspectral imagery based on causal processing[J]. Journal of Infrared and Millimeter Waves, 2015, 34(1): 114 Copy Citation Text show less
    References

    [1] Chang C-I, Hsueh M. Characterization of anomaly detection for hyperspectral imagery [J]. Sensor Review,2006,26(2): 137-146.

    [2] Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution [J]. IEEE Trans. on Acoustic, Speech and Signal Process.1990,38(10): 1760-1770.

    [3] Tarabalka Y, Haavardsholm T V, Kasen I, et al. Real-time anomaly detection in hyperspectral images using multivariate normal mixture, pdels and GPU processing [J]. J. Real Time Image Processing,2009,4(3): 287-300.

    [4] Haavardsholm T V, Arisholm G, Kavara A, et al. Architecture of the real-time target detection processing in an airborne hyperspectral demonstrator system [C]. 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), June 2010.14-16.

    [5] Skauli T, Haavardsholm T, Kasen I, et al. Hyperspectral imaging technology and systems, exemplified by airborne real-time target detection [C]. 2011 Conference on Lasers and Electro-Optics (CLEO), May 2011.1-6.

    [6] Chang C-I Chiang S-S. Anomaly detection and classification for hyperspectral imagery [J].IEEE Trans. on Geoscience and Remote Sensing.2002,40(2),1314-1325.

    [7] Kailath T, Linear Systems [M], Prentice-Hall,1980.655.

    [8] Wang J, Chang C-I . Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery [J]. IEEE Trans. on Geoscience and Remote Sensing,2006,44(9),2601-2616.

    [9] Chang Y-C. Ren H, Chang, C-I, et al. How to design synthetic images to validate and evaluate hyperspectral imaging algorithms [C], SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, March 16-20, Orlando, Florida, 2008.

    CLP Journals

    [1] FU Li-ting, DENG He, LIU Chun-hong. Fast Anomaly Detection Algorithm for Hyperspectral Imagery Based on Line-by-line Processing[J]. Acta Photonica Sinica, 2017, 46(4): 410003

    [2] Fu Liting, Deng He, Liu Chunhong. Novel Fast Real-Time Target Detection and Classification Algorithms for Hyperspectral Imagery[J]. Acta Optica Sinica, 2017, 37(2): 230002

    ZHAO Chun-Hui, WANG Yu-Lei, LI Xiao-Hui. A real-time anomaly detection algorithm for hyperspectral imagery based on causal processing[J]. Journal of Infrared and Millimeter Waves, 2015, 34(1): 114
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