基于空谱联合聚类的改进核协同高光谱异常检测
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摘要
针对空谱信息中普遍存在的异常干扰现象,提出了基于空谱联合聚类的自适应核协同表示高光谱异常目标探测算法.算法充分发挥了基于密度的聚类算子(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)对于异常点的筛选特性,在DBSCAN聚类去除异常波谱的基础上,采用分波段子集随机投影变换对数据降维处理,以减少谱噪声和谱冗余,并采用DBSCAN聚类消除了局部背景像元中的杂乱点对协同探测算法结果的干扰.研究了背景离散度对核参选择的影响,比较了不同的核估计方法,并提出基于平均差的自适应核协同算法.采用该方法对AVIRIS和ROSIS的三组数据进行仿真实验并与现有算法进行了对比,结果表明该算法表现出较好的探测性能.
An adaptive-kernel collaborative representation method based on spatial-spectral joint clustering for hyperspectral anomaly detection is proposed,which is well used to solve the abnormal interference in space-spectrum information.The algorithm gives full play to the filtering characteristic of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)for outliers,and is applied to space-spectrum processing.On the basis of removing abnormal spectrum by DBSCAN clustering,random projection for reserved subsets is used to reduce the dimension of the data,so spectral noise and spectral redundancy can be solved properly.Considering the influence of background outliers on collaborative representation detection algorithm,DBSCAN clustering is used to remove the clutter points in the local background pixels.Furthermore,the influence of background dispersion on the selection of kernel parameters is studied.By comparing different kernel estimation methods,an adaptive kernel measure method based on average difference is proposed.The proposed algorithm is used to simulate three sets of AVIRIS and ROSIS data and compared with the international mainstream anomaly detection algorithm,the results show that the proposed algorithm has a good detection performance.
An adaptive-kernel collaborative representation method based on spatial-spectral joint clustering for hyperspectral anomaly detection is proposed,which is well used to solve the abnormal interference in space-spectrum information.The algorithm gives full play to the filtering characteristic of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)for outliers,and is applied to space-spectrum processing.On the basis of removing abnormal spectrum by DBSCAN clustering,random projection for reserved subsets is used to reduce the dimension of the data,so spectral noise and spectral redundancy can be solved properly.Considering the influence of background outliers on collaborative representation detection algorithm,DBSCAN clustering is used to remove the clutter points in the local background pixels.Furthermore,the influence of background dispersion on the selection of kernel parameters is studied.By comparing different kernel estimation methods,an adaptive kernel measure method based on average difference is proposed.The proposed algorithm is used to simulate three sets of AVIRIS and ROSIS data and compared with the international mainstream anomaly detection algorithm,the results show that the proposed algorithm has a good detection performance.
引文
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[4] REED I S,YU X.Adaptive multiple-band CFAR detection of an optical pattern with unknown spectraldistribution[J].IEEE Transactions on Acoustics Speech&Signal Processing,1990,38(10):1760-1770.
[5] TAITANO Y P,GEIER B A,BAUER K W.A locally adaptable iterative RXdetector[J].Eurasip Journal on Advances in Signal Processing,2010,2010(1):341908.
[6] MOLERO J M,GARZN E M,GARCA I,et al.Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2013,6(2):801-814.
[7] IMANI M.RX anomaly detector with rectifiedbackground[J].IEEE Geoscience&Remote Sensing Letters,2017,14(8):1313-1317.
[8] KWON H,NASRABADI N M.Kernel RX-algorithm:a nonlinear anomaly detector forhyperspectral imagery[J].IEEE Transactions on Geoscience&Remote Sensing,2005,43(2):388-397.
[9] BANERJEE A,BURLINA P,DIEHL C.A support vector method for anomaly detection inhyperspectral imagery[J].IEEE Transactions on Geoscience&Remote Sensing,2006,44(8):2282-2291.
[10] KHAZAI S,HOMAYOUNI S,SAFARI A,et al.Anomaly detection in hyperspectral images based on an adaptive support vector method[J].IEEE Geoscience&Remote Sensing Letters,2011,8(4):646-650.
[11] LI W,DU Q.Collaborative representation forhyperspectral anomaly detection[J].IEEE Transactions on Geoscience&Remote Sensing,2015,53(3):1463-1474.
[12] LI J,ZHANG H,ZHANG L,et al.Hyperspectral anomaly detection by the use of background joint sparse representation[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2015,8(6):2523-2533.
[13] ZHANG W,LI X,DOU Y,et al.A geometry-based band selection approach for hyperspectral image analysis[J].IEEE Transactions on Geoscience&Remote Sensing,2018,56(8):4318-4333.
[14] SHEN J,HAO X,LIANG Z,et al.Real-time superpixel segmentation by DBSCAN clustering algorithm[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2016,25(12):5933-5942.
[15] HOU J,GAO H,LI X.DSets-DBSCAN:aparameter-free clustering algorithm[J].IEEE Transactions on Image Processing,2016,25(7):3182-3193.
[16] JIN Jing,ZOU Zheng-rong,TAO Chao.Compressed texton based high resolution remote sensing image classification[J].Acta Geodaetica Et Cartographica Sinica,2014,43(5):493-499.金晶,邹峥嵘,陶超.高分辨率遥感影像的压缩纹理元分类[J].测绘学报,2014,43(5):493-499.
[17] VAFADAR M, GHASSEMIAN H.Anomaly detection ofhyperspectral imagery using modified collaborative representation[J].IEEE Geoscience&Remote Sensing Letters,2018(99):1-5.
[18] KHAZAI S,HOMAYOUNI S,SAFARI A,et al.Anomaly detection in hyperspectral images based on an adaptive support vector method[J].IEEE Geoscience&Remote Sensing Letters,2011,8(4):646-650.
[19] TANG Yi-dong,HUANG Shu-lin,LING Qiang,et al.Adaptive kernel collaborative representation anomaly detection for hyperspectral imagery[J].High Power Laser&Particle Beams,2015,27(9):49-55.唐意东,黄树彩,凌强,等.高光谱图像自适应核联合表示异常检测[J].强激光与粒子束,2015,27(9):49-55.
[20] MEI Feng,ZHAO Chun-hui,WANG Li-guo,et al.Support vector data description based on adaptive anomaly detection method in hyperspectral imagery[J].Acta Photonica Sinica,2009,38(11):2820-2825.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.