[1] HU Yan, WANG Hui-qin, MA Zong-fang, et al. Image fire detection based on independent component analysis and support vector machine[J]. Journal of Computer Applications, 2012, 32(3): 889-892.
[3] DU B, ZHANG Y, ZHANG L, et al. Beyond the sparsity-based target detector: a hybrid sparsity and statistics based detector for hyperspectral images[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5345-5357.
[5] ZHAO Chun-hui, LI Xiao-hui, WANG Yu-lei. Research advance on anomaly detection for hyperspectral imagery[J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(8): 803-811.
[7] 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.
[8] REED I S, YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustic, Speech and Signal Process, 1990, 38(10): 1760-1770.
[9] CHANG C I, CHIANG S S. Anomaly detection and classification for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(2): 1314-1325.
[11] 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 Electronic Systems, 2014, 50(2): 1511-1534.
[12] 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 Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1376-1389.
[13] ZHAO Chun-hui, WANG Yu-lei, QI Bin, et al. Global and local real-time anomaly detectors for hyperspectral remote sensing imagery[J]. Remote Sensing, 2015, 7: 3966-3985.
[14] DU B, ZHANG L. A discriminative metric learning based anomaly detection method[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 6844-6857.
[15] ZHAO Chun-hui, YOU Wei, QI Bin, et al. Real-time anomaly detection algorithm for hyperspectral remote sensing by using recursive polynomial kernal function[J]. Acta Optica Sinica, 2016, 36(2): s228002.
[16] WANG Y L, SCHULTZ R, CHEN S Y, et al. Progressive constrained energy minimization for subpixel detection[C]. SPIE, 2013, 8743: 874321.
[17] 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.
[18] CHANG Y C, REN H, CHANG C I, et al. How to design synthetic images to validate and evaluate hyperspectral imaging algorithms[C]. Algorithms and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery XIV, 2008, 6966: 69661P.