[1] Wang Xiaofei, Yan Qiujing, Zhang Junping, et al. Super-resolution reconstruction algorithm based on relevance vector machine for hyperspectral image［J］. Chinese J Lasers, 2014, 41(s1): s114001.
[2] Wang Xiaofei, Yan Qiujing. An ensemble learning algorithm for one-class classification of hyperspectral images［J］. Acta Optica Sinica, 2014, 34(s2): s211002.
[3] Zhao Chunhui, You Wei, Qi Bin, et al. Real-time anomaly detection algorithm for hyperspectral remote sensing by using recursive polynomial kernel function［J］. Acta Optica Sinica, 2016, 36(2): 0228002.
[4] Wang Liguo, Zhao Chunhui. Hyperspectral image processing technology［M］. Beijing: National Defence Industry Press, 2013.
[5] Zhang Bing, Gao Lianru. Hyperspectral image classification and target detection［M］. Beijing: Science Press, 2011.
[6] Reed I S, Yu X. Adaptive multi-band CFAR detection of an optical pattern with unknown spectral distribution［J］. IEEE Transactions on Acoustics Speech and Signal Processing, 1990, 38(10): 1760-1770.
[7] Chen S Y, Wang Y L, Wu C C, et al. Real-time causal processing of anomaly detection for hyperspectral imagery［J］. IEEE Transactions on Aerospace and Electronuc Systems, 2014, 50(2): 1511-1534.
[8] Chang C I, Li Y, Hobbs M C, et al. Progressive band processing of anomaly detection in hyperspectral imagery［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3558-3571.
[9] Rossi A, Acito N, Diani M, et al. RX architectures for real-time anomaly detection in hyperspectral images［J］. Journal of Real-Time Image Processing, 2014, 9(3): 503-517.
[10] Zhang Xianda. Matrix analysis and applications［M］. Beijing: Tsinghua University Press, 2004.
[11] Chang C I, Wang Y L, Chen S Y. Anomaly detection using causal sliding windows［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3260-3270.
[12] Kossi A, Acito N, Diani M, et al. RX architecture for real-time anomaly detection in hyperspectral images［J］. Journal of Real-Time Image Processing, 2014, 9(3): 503-517.
[13] Wang J, Chang C I. Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44( 9): 2601-2616.
[14] Chang Y C, Ren H, Chang C I, et al. How to design synthetic images to validate and evaluate hyperspectral imaging algorithms［C］. SPIE, 2008, 6966: 69661P.
