基于边缘扩展和局部求和的高光谱异常目标检测
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摘要
针对使用滑动窗不易检测高光谱图像边缘像元的缺陷,采用边缘扩展的方法使得边缘像元能够得到正常检测;而后在滑动窗内采用局部求和的方式建立高光谱图像数据模型,提高了检测的合理性;最后为验证算法的有效性,利用真实的高光谱数据进行实验,结果表明该算法优于传统RX算法和KRX算法,能检测到更多的异常目标,且降低了虚警率。
Considering that it's not easy to detect the defect of the pixels on the edge of hyperspectral image with slide window,we used edge expansion method to make the pixels on the edge be detected normally.Then,a new hyperspectral data model was established in the form of local summation in slide window,hence the rationality was promoted.At last,to prove the effectiveness of the proposed algorithm,experiments were implemented with real data.The results show that this algorithm performs better than the traditional algorithms of RX and KRX,and it can detect more anomalies with lower false alarm rate.
Considering that it's not easy to detect the defect of the pixels on the edge of hyperspectral image with slide window,we used edge expansion method to make the pixels on the edge be detected normally.Then,a new hyperspectral data model was established in the form of local summation in slide window,hence the rationality was promoted.At last,to prove the effectiveness of the proposed algorithm,experiments were implemented with real data.The results show that this algorithm performs better than the traditional algorithms of RX and KRX,and it can detect more anomalies with lower false alarm rate.
引文
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[10]李杰,赵春晖,梅锋.利用背景残差数据检测高光谱图像异常[J].红外与毫米波学报,2010,29(2):150-155.
[2]ASHTON E A,SCHUAM A.Algorithms for the detection of sub-pixel targets in multispectral imagery[J].Photogrammetric Engineering and Remote Sensing,1998,64(7):723-731.
[3]赵春晖,王佳,王玉磊.采用背景抑制和自适应阈值分割的高光谱异常目标检测[J].哈尔滨工程大学学报,2016,37(2):278-283.
[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]高昆,刘莹,王丽静,等.基于高斯马尔科夫模型的高光谱异常目标检测算法研究[J].光谱学与光谱分析,2015,35(10):2846-2848.
[6]BANERJEE A,BURLINA P.A support vector method for anomaly detection in hyperspectral imagery[J].IEEETransactions on Geoscience Remote sensing,2006,44(8):2282-2285.
[7]ZARE-BAGHBIDI M,HOMAYOUNI S,JAMSHIDI K.Improving the RX anomaly detection algorithm for hyperspectral images using FFT[J].Journal of Modeling&Simulation in Electrical&Electronics Engineering,2015,1(2):33-38.
[8]梅锋.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.
[9]JABLONSKI J A,BIHL T J,BAUER K W.Principal component reconstruction error for hyperspectral anomaly detection[J].IEEE Geoscience and Remote Sensing Letters,2015,12(8):1725-1728.
[10]李杰,赵春晖,梅锋.利用背景残差数据检测高光谱图像异常[J].红外与毫米波学报,2010,29(2):150-155.