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国土资源遥感  2014, Vol. 26 Issue (3): 1-7    DOI: 10.6046/gtzyyg.2014.03.01
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高光谱图像异常目标检测算法研究与进展
成宝芝
大庆师范学院物理与电气信息工程学院, 大庆 163712
Study and progress of anomaly target detection in hyperspectral imagery
CHENG Baozhi
College of Physics and Electrical Information Engineering, Daqing Normal University, Daqing 163712, China
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摘要 

高光谱图像是一种新型的具有“图谱合一”特性的遥感图像,其连续的光谱曲线可更好地表达地表物质间的细微差异,在地表物质的分类、解混和目标探测等方面得到了广泛应用。随着高光谱遥感技术的深入发展,对不需要先验信息的异常目标检测的研究成为最活跃的方向之一,许多研究者提出了具有较好效果的异常检测算法。基于对国内外已有算法的综合归纳和分析,系统地论述了高光谱异常检测的研究现状和最新进展。阐述了高光谱异常目标检测的实质和基本理论;从算法思想、关键技术和优缺点等方面重点分析总结了较有代表性的异常目标检测算法,并对其进行了概括和阐述;最后对异常检测算法的未来研究方向进行了展望,力图为高光谱异常目标检测算法研究找到新的突破点。

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张修远
刘修国
关键词 随机森林(RF)模糊分类高维特征    
Abstract

Hyperspectral image is a new kind of remote sensing images with the feature of "combining mapping and spectra into one",thus better expressing the subtle differences on the surface of the material through the continuous spectral curve. Hyperspectral images have a wide range of applications in such aspects as classification,unmixing and target detection. With the continuous development of hyperspectral remote sensing technology,anomaly target detection has become one of the most active direction of research because it doesn't need a priori information. Many anomaly target detection algorithms have been proposed. Based on data available both in China and abroad,this paper summarized the research situation and new progress in anomaly detection algorithms. The author first expounded the essence of hyperspectral anomaly target detection and used the basic theory and then analyzed and summed up some representative anomaly detection algorithms in such aspects as the ideas of algorithm,key technology,advantages and disadvantages. On such a basis, the author summarized and described the evaluation method of anomaly detection and discussed the future development trend of anomaly target detection algorithm, with the purpose of finding new breakthroughs in the study of the algorithm of hyperspectral anomaly target detection.

Key wordsrandom forest(RF)    fuzzy classification    high dimensional features
收稿日期: 2013-07-09      出版日期: 2014-07-01
:  TP751.1  
基金资助:

大庆师范学院科学研究基金项目(编号:11ZR09)资助。

作者简介: 成宝芝(1976-),男,博士,讲师,主要从事高光谱图像处理研究。Email:chengbaozhigy@163.com。
引用本文:   
成宝芝. 高光谱图像异常目标检测算法研究与进展[J]. 国土资源遥感, 2014, 26(3): 1-7.
CHENG Baozhi. Study and progress of anomaly target detection in hyperspectral imagery. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 1-7.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.03.01      或      https://www.gtzyyg.com/CN/Y2014/V26/I3/1
[1] 梅峰.基于核机器学习的高光谱异常目标检测算法研究[D].哈尔滨:哈尔滨工程大学,2009. Mei F.Research on kernel machine learning based anomaly detection algorithms in hyperspectral imagery[D].Harbin:Harbin Engineering University,2009.
[2] Reed I S,Yu X L.Adaptive multiple-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.
[3] Yu X L,Hoff L E,Reed I S,et al.Automatic target detection and recognition in multiband imagery:A unified ML detection and estimation approach[J].IEEE Transactions on Image Processing,1997,6(1):143-156.
[4] Kwon H,Nasrabadi N M.Kernel RX-algorithm:A nonlinear anomaly detector for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(2):388-397.
[5] Banerjee A,Burlina P,Diehl C.A support vector method for anomaly detection in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2282-2291.
[6] 谷延锋,刘颖,贾友华,等.基于光谱解译的高光谱图像奇异检测算法[J].红外与毫米波学报,2006,25(6):473-477. Gu Y F,Liu Y,Jia Y H,et al.Anomaly detection algorithm of hyperspectral images based on spectral analyses[J].Journal of Infrared and Millimeter Waves,2006,25(6):473-477.
[7] 李杰,赵春晖,梅锋.利用背景残差数据检测高光谱图异常[J].红外与毫米波学报,2010,29(2):150-155. Li J,Zhao C H,Mei F.Detecting hyperspectral anomaly by using background residual error data[J].Journal of Infrared and Millimeter Waves,2010,29(2):150-155.
[8] Carlotto M J.A cluster-based approach for detecting man-made objects and changes in imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(2):374-387.
[9] 成宝芝,赵春晖,王玉磊.结合光谱解混的高光谱图像异常目标检测SVDD算法[J].应用科学学报,2012,30(1):82-88. Cheng B Z,Zhao C H,Wang Y L.SVDD algorithm with spectral unmixing for anomaly detection in hyperspectral images[J].Journal of Applied Sciences,2012,30(1):82-88.
[10] Chang C I,Chiang S S.Anomaly detection and classification for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(6):1314-1325.
[11] Stein D W J,Beaven S G,Hoff L E,et al.Anomaly detection from hyperspectral imagery[J].IEEE Signal Processing Magazine,2002,19(1):58-69.
[12] Stein D W J.Stochastic compositional models applied to subpixel analysis of hyperspectral imagery[J].Proceedings of SPIE,2002,4480:49-56.
[13] Fowler J E,Du Q.Anomaly detection and reconstruction from random projections[J].IEEE Transactions on Image Processing,2012,21(1):184-195.
[14] Matteoli S,Diani M,Corsini G.Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images[J].Optical Engineering,2010,49(4):046201-1-046201-6.
[15] Matteoli S,Diani M,Corsini G.Hyperspectral anomaly detection with kurtosis-driven local covariance matrix corruption mitigation[J].IEEE Geoscience and Remote Sensing Letters,2011,8(3):532-536.
[16] Molero J M,Paz A,Garzón E M,et al.Fast anomaly detection in hyperspectral images with RX method on heterogeneous clusters[J].The Journal of Supercomputing,2011,58(3):411-419.
[17] Ren H,Chen C W,Chen H T.Weighted anomaly detection for hyperspectral remotely sensed images[C]//Proc.SPIE The International Society for Optical Engineering,2005,5995:599501-599507.
[18] Riley R A,Newsom R K,Andrews A K.Anomaly detection in noisy hyperspectral imagery[C]//USA:SPIE-Int.Soc.Opt.Eng,2004,5546:159-170.
[19] 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,2012,11:12-26.
[20] Du B,Zhang L P.Random-selection-based anomaly detector for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(5):1578-1589.
[21] Broadwater J,Chellappa R.Hybrid detectors for subpixel targets[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(11):1891-1903.
[22] Scholkopf B,Smola A J.Learning with kernels:Support vector machines regularization,optimization and beyond[M].Cambridge,MA:MIT Press,2001.
[23] Kwon H,Nasrabadi N M.A comparative study of kernel spectral matched signal detectors for hyper-spectral target detection[C]//The International Society for Optical Engineering.USA:SPIE,2005,5806:827-838.
[24] Kwon H,Nasrabadi N M.Kernel adaptive subspace detector for hyperspectral target detection[C]//International Conference on Acoustics,Speech,and Signal Processing.USA:IEEE,2005,4:681-684.
[25] Kwon H,Nasrabadi N M.Kernel-based subpixel target detection in hyperspectral images[C]//International Joint Conference on Neural Networks.USA:IEEE,2004,1:717-721.
[26] 赵春晖,李杰,梅锋.核加权RX高光谱图像异常检测算法[J].红外与毫米波学报,2010,29(5):378-382. Zhao C H,Li J,Mei F.A kernel weighted RX algorithm for anomaly detection in hyperspectral imagery[J].Journal of Infrared and Millimeter Waves,2010,29(5):378-382.
[27] 赵春晖,胡春梅,包玉刚.一种背景误差累积的高光谱图像异常检测算法[J].光子学报,2010,39(10):1830-1835. Zhao C H,Hu C M,Bao Y G.A kind of anomaly detection algorithm for hyperspectral image through background error cumulation[J].Acta Photonica Sinica,2010,39(10):1830-1835.
[28] 梅锋,赵春晖.基于空域滤波的核RX高光谱图像异常检测算法[J].哈尔滨工程大学学报,2009(6):697-702. Mei F,Zhao C H.Spatial filter based anomaly detection algorithm for hyperspectral imagery kernel RX detectors[J].Journal of Harbin Engineering University,2009(6):697-702.
[29] 赵春晖,胡春梅.基于目标正交子空间投影加权的高光谱图像异常检测算法[J].吉林大学学报:工学版,2011,41(5):1468-1474. Zhao C H,Hu C M.Weighted anomaly detection algorithm for hyperspectral image based on target orthogonal subspace projection[J].Journal of Jilin University:Engineering and Technology Edition,2011,41(5):1468-1474.
[30] 史振威,吴俊,杨硕,等.RX及其变种在高光谱图像中的异常检测[J].红外与激光工程,2012,41(3):796-802. Shi Z W,Wu J,Yang S,et al.RX and its variants for anomaly detection in hyperspectral images[J].Infrared and Laser Engineering,2012,41(3):796-802.
[31] 谌德荣,宫久路,何光林,等.高光谱图像全局异常检测RFS-SVDD算法[J].宇航学报,2010,31(1):228-232. Chen D R,Gong J L,He G L,et al.A RFS-SVDD algorithm for hyperspectral global anomaly detection[J].Journal of Astronautics,2010,31(1):228-232.
[32] Khazai S,Homayouni S,Safari A.Anomaly detection in hyperspectral images based on an adaptive support vector method[J].IEEE Geoscience and Remote Sensing Letters,2011,8(4):646-650.
[33] 谌德荣,张立燕,陶鹏,等.结合邻域聚类分割的高光谱图像异常检测支持向量数据描述方法[J].宇航学报,2007,28(3):767-771. Chen D R,Zhang L Y,Tao P,et al.Support vector data description for anomaly detection in hyperspectral imagery combined with neighbor-ring clustering segmentation[J].Journal of Astronautics,2007,28(3):767-771.
[34] 梅锋,赵春晖,王立国,等.基于支持向量描述的自适应高光谱异常检测算法[J].光子学报,2009,38(11):2820-2825. Mei F,Zhao C H,Wang L G,et al.Support vector data description based on adaptive anomaly detection method in hyperspectral imagery[J].Acta Photonica Sinica,2009,38(11):2820-2825.
[35] Gurram P,Kwon H,Han T.Sparse kernel-based hyperspectral anomaly detection[J].IEEE Geoscience and Remote Sensing Letters,2012,9(5):943-947.
[36] Khazai S,Safari A,Mojaradi B,et al.A fast-adaptive support vector method for full-pixel anomaly detection in hyperspectral images[C]//IEEE International Geoscience and Remote Sensing Symposium.Vancouver,BC,Canada:IEEE,2011:1763-1767.
[37] 谌德荣,宫久路,陈乾,等.基于样本分割的快速高光谱图像异常检测支持向量数据描述方法[J].兵工学报,2008,29(9):1049-1053. Cheng D R,Gong J L,Chen Q,et al.Support vector data description for fast anomaly detection in hyperspectral imagery based on sample segmentation[J].Acta Armamentarii,2008,29(9):1049-1053.
[38] Zhao C H,Wang Y L,Mei F.Kernel ICA feature extraction for anomaly detection in hyperspectral imagery[J].Chinese Journal of Electronics,2012,21(2):265-269.
[39] Goldberg H,Kwon H,Nasrabadi N M.Kernel eigenspace separation transform for subspace anomaly detection in hyperspectral imagery[J].IEEE Geoscience and Remote Sensing Letters,2007,4(4):581-585.
[40] Schweizer S M,Moura J M F.Hyperspectral imagery:Clutter adaptation in anomaly detection[J].IEEE Transactions on Information Theory,2000,46(5):1855-1871.
[41] Schweizer S M,Moura J M F.Efficient detection in hyperspectral imagery[J].IEEE Transactions on Image Processing,2001,10(4):584-597.
[42] 张立燕,谌德荣,李世义,等.基于低概率检测的高光谱图像有损压缩方法研究[J].弹箭与制导学报,2008,28(1):307-310. Zhang L Y,Cheng D R,Li S Y,et al.Research on hyperspectral imagery loss compression method based on low probability detection[J].Journal of Projectiles,Rockets,Missiles and Guidance,2008,28(1):307-310.
[43] 王玉磊,赵春晖,王江洪.基于低概率检测的高光谱异常目标检测算法研究[J].黑龙江大学自然科学学报,2010,27(3):411-416. Wang Y L,Zhao C H,Wang J H.Anomaly detection based on low probability detection for hyperspectral image[J].Journal of Natural Science of Heilongjiang University,2010,27(3):411-416.
[44] He L,Pan Q,Di W,et al.Anomaly detection in hyperspectral imagery based on maximum entropy and nonparametric estimation[J].Pattern Recognition Letters,2008,29(9):1392-1403.
[45] Gao G.A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images[J].IEEE Geoscience and Remote Sensing Letters,2011,8(3):557-561.
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