高光谱遥感影像亚像元小目标探测研究
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摘要
高光谱遥感影像具有光谱分辨率高、图谱合一的特点,可以提供区分不同物质的诊断性光谱信息,因此在地物目标探测领域具有独特的优势。由于地物分布情况复杂、传感器空间分辨率的限制、目标数目少尺寸小等原因,待探测目标通常与其他地物共同组成混合像元,这时目标以信号比较弱小的亚像元形式存在,问题转化为在像元内部确定目标信号存在性的亚像元小目标探测问题。高光谱遥感影像的亚像元小目标探测问题是高光谱信息处理的前沿问题,也是制约高光谱影像应用的难点问题。本文针对高光谱影像亚像元小目标探测中存在的数据维数较大：目标信号的光谱变化现象严重；目标对背景统计信息的干扰等问题进行深入研究,提出了多种亚像元目标探测方法。具体地,本文的主要研究工作可以概括为：
(1)总结了信号检测理论的一般规律,及其在高光谱遥感影像目标探测问题中的应用,推导并分析了三类在高光谱遥感影像目标探测中应用广泛的探测理论：基于广义似然比理论探测器、恒虚警率探测器和匹配滤波器探测方法；总结了线性混合模型反映地物信息方面的物理含义、表达形式和主要局限等,研究了基于光谱分解的目标探测方法,推导了基于主动/被动子集合方法的全约束最小二乘数值解法,通过实验指出了基于光谱分解目标探测方法的缺陷。
(2)提出一种基于最小噪声分离变换的约束能量最小化探测方法,在降低维数的同时分离噪声、保持目标与背景光谱差异,以克服因高光谱遥感数据维数大造成的探测速度过慢问题。实验结果表明,与传统的约束能量最小化方法相比,该方法探测亚像元目标效果更好、运算速度更快。
(3)提出一种基于光谱分解的自适应匹配子空间探测方法,将基于光谱分解的组分信息替代子空间参数估计的结果,利用具有物理意义的端元和组分信息建立目标与背景子空间,进而构造子空间匹配探测器,利用子空间策略克服目标光谱变化问题、使用结构化背景提高探测精度。实验证明,与自适应匹配子空间探测器等方法相比,该方法可以更好地表达影像的背景信息、提高目标与背景的可分离度。
(4)提出一种基于局域信息的正交子空间投影探测方法,利用局域内的相邻像元构建正交投影算子,与传统正交子空间投影方法中使用影像全局的端元构造投影算子相区别。实验中发现,基于局域信息的正交子空间投影方法与传统正交子空间方法相比,投影后目标相对背景的值域更大,这反映出：利用目标周边局域内的像元作为基本元素构建正交子空间,可以更好地反映目标与背景的差异；此外实验还表明,基于光谱分解的自适应匹配子空间探测方法,与正交子空间投影方法、基于局域信息的正交子空间投影方法、和自适应匹配子空间探测方法相比,探测结果更优。
(4)提出一种背景非结构化混合探测器,利用统计分布特征表示背景信息,结合光谱分解结果构建广义似然比探测算子。此外,为了克服传统探测器对所有像元的端元数目保持不变的缺陷,引入端元可变的策略判断待探测像元中包含背景端元的种类,分别提出一种基于端元可变的背景结构化混合探测器和一种基于端元可变的背景非结构化混合探测器,提高了光谱分解精度以及背景信息表征的精度。分别利用ROC曲线等评价指标对加入端元可变策略后探测器分离目标和背景效果、对背景端元估计偏差的敏感度等情况进行比较和分析,实验结果验证了端元可变策略的有效性,这也证明了通过提高表征背景精度的方式可以提高亚像元目标的探测效果。
(5)提出一种基于随机采样的异常目标探测方法,在缺乏先验目标信息的条件下,解决目标探测问题。该方法利用多个局域位置选取的像元作为背景信息,结合局域特征和全局特征,通过多次选取背景信息来建立不同的探测算子,最后融合多次探测结果,利用异常目标呈现异常次数最多的特点,确定异常目标。实验证明,该方法可以较好地抑制目标对背景信息的干扰,提高目标与背景的可分度。此外,还提出了一种实时探测的随机采样异常目标探测方法,实验证明其可以减少不同背景交界处的虚警数目。
Imaging spectrometer can provide images with very high spectral resolution by collecting many spectrally continuous images, named hyperspectral images, at the same time. Different materials are distinguishable by the spectral difference revealed in hyperspectral images. The spatial pattern of the images is also helpful to position the potential target with spectral difference from the background. These characteristics make hyperspectral images suitable for target detection task. However, due to the complex distribution of different ground objects and the limited spatial resolution of the hyperspectral images, a pixel in the hyperspectral images is usually composed of different land objects and the targets usually reside in the sub-pixel scale. Sub-pixle target detection is a difficulty for extracting information from hyperspectral images and has been of great interest to the researchers in target detection domain since the end of last century. This thesis focuses on the following problems in sub-pixel target detection from hyperspectral images:detection with low dimension data, target spectral variety, contamination caused by targets to the background statistics.
The main research work and the corresponding contributions of this dissertation are as following:
Target detection in hyperspectral images originates from the singal detection theory. So the singal processing theory is summarized. The signals received by the imaging spectrometer come from the array of multiple senors acquiring different radiances from the same ground scene. This is the array signal processing in hyperspectral images. Besides, the three most important methods for constructing the target detectors are detailed:generalized likelihood ratio test, constant false alarm rate detector and matched detector theory.
Linear mixture model is the most widely used model for target detection in hyperspectral images. The physical basis, the description and the limitation of the model are summarized. Based on the linear mixture model, the linear spectral unmixing based target detection method is introduced. The fully constrained least squares method is used to solve the problem and an active/passive sets based numerical calculation method is researched on. Experiments show the limitation of the method. Besides, a constrained energy minimization method based on minimum noise fraction transformation (MNF-CEM) is proposed. In this method, minimum noise fraction (MNF) reduces the dimension of the hyperspectral imageries and separates the noise from the hyperspectral images. Then a finite impulse response (FIR) filter is used to detect the sub-pixel targets in the low dimension images. In this way, the computations of ill-conditional matrix inverse and virtual dimension of the hyperspectral imageries are unnecessary. Experiments show that this method can restrain the influence of the noise and is an effective sub-pixel target detection method for hyperspectral imageries.
Subspace based methods can solve the spectral variety problem. Two subspace based detection methods are proposed in this paper. Traditional matched subspace detectors rely on the estimated endmembers and the according abundance to model the background and construct a generalized likelihood ratio test based detector. However, these endmembers and abundances have no physical meanings. So we use the abundances from the spectral unmixing so as to introduce the physically meaningful endmembers and abundances into the matched subspace detector. The proposed method is called spectral unmixing based matched subspace detector. The other method is the local OSP (Orthogonal Subspace Projection) method which uses the local neighbor pixels to represent the background information of the pixels under observation instead of the endmembers from the whole image. Experiments show that with physically meaningful endmembers and the according abundances the proposed matched subspace detector models the background better and performs better than its counterpart detector and the other OSP detectors; local OSP output a better result than OSP which proves that the pixel is related more with the local information than the whole set of pixels in the image, so it is more suitable to model the pixel's background with its neighboring pixels.
To further investigate the performance with more accurate background information, two hybrid detectors are proposed. Both of them make use of abundances from spectral unmixing, and a background endmembers selection procedure is introduced into the detectors. The aim of the background endmembers selection is to use the correct kinds of background endmembers in the detection, while traditional methods use the same endmembers in the detection of each pixel. A structured background hybrid detector and an unstructured background hybrid detector are developed. First, the endmembers selection procedure is performed to choose the correct kinds of background endmembers in each pixel. Second, spectral unmixing is done with the endmembers selection information to get more accurate abundances. Then, the abundances are used to construct the structured background hybrid detector and the unstructured background hybrid detector and the endmembers selection information is taken into consideration in the structured detector again. Experiments show that the hybrid detectors with endmembers selection procedure perform better than the hybrid ones without endmembers selection procedure. It is concluded that with more accurate information to model the reality of the background ground objects the detection performance would be improved.
Current anomaly detection methods are susceptiable to the anomalies in the background statistics. And they either use a local background statistics or a global statistics. A random selection based anomaly detector is proposed in this thesis. The method uses a random selection procedure to get background statistics from the image. Eachtime, not only the local neighboring pixles but also a number of blocks containting the same number of pixels are selected from random positions of the image. In this way, both local and global statistics of the pixel under observation are taken into consideration. The selection procedure is performed several times and each time the detection procedure is done successively with the constructed background statistics. Finally, the detection results are confused. Besides, the real-time version of the random selection based detector is implemented. Experiments show that the separability between targets and background by the proposed random selection based anomaly detector is larger than that by SSRX detector. And the performance of the real-time version detector also shows better detection and less false alarms on the boundaries of different backgrounds than current real-time anomaly detectors..
(1)总结了信号检测理论的一般规律,及其在高光谱遥感影像目标探测问题中的应用,推导并分析了三类在高光谱遥感影像目标探测中应用广泛的探测理论：基于广义似然比理论探测器、恒虚警率探测器和匹配滤波器探测方法；总结了线性混合模型反映地物信息方面的物理含义、表达形式和主要局限等,研究了基于光谱分解的目标探测方法,推导了基于主动/被动子集合方法的全约束最小二乘数值解法,通过实验指出了基于光谱分解目标探测方法的缺陷。
(2)提出一种基于最小噪声分离变换的约束能量最小化探测方法,在降低维数的同时分离噪声、保持目标与背景光谱差异,以克服因高光谱遥感数据维数大造成的探测速度过慢问题。实验结果表明,与传统的约束能量最小化方法相比,该方法探测亚像元目标效果更好、运算速度更快。
(3)提出一种基于光谱分解的自适应匹配子空间探测方法,将基于光谱分解的组分信息替代子空间参数估计的结果,利用具有物理意义的端元和组分信息建立目标与背景子空间,进而构造子空间匹配探测器,利用子空间策略克服目标光谱变化问题、使用结构化背景提高探测精度。实验证明,与自适应匹配子空间探测器等方法相比,该方法可以更好地表达影像的背景信息、提高目标与背景的可分离度。
(4)提出一种基于局域信息的正交子空间投影探测方法,利用局域内的相邻像元构建正交投影算子,与传统正交子空间投影方法中使用影像全局的端元构造投影算子相区别。实验中发现,基于局域信息的正交子空间投影方法与传统正交子空间方法相比,投影后目标相对背景的值域更大,这反映出：利用目标周边局域内的像元作为基本元素构建正交子空间,可以更好地反映目标与背景的差异；此外实验还表明,基于光谱分解的自适应匹配子空间探测方法,与正交子空间投影方法、基于局域信息的正交子空间投影方法、和自适应匹配子空间探测方法相比,探测结果更优。
(4)提出一种背景非结构化混合探测器,利用统计分布特征表示背景信息,结合光谱分解结果构建广义似然比探测算子。此外,为了克服传统探测器对所有像元的端元数目保持不变的缺陷,引入端元可变的策略判断待探测像元中包含背景端元的种类,分别提出一种基于端元可变的背景结构化混合探测器和一种基于端元可变的背景非结构化混合探测器,提高了光谱分解精度以及背景信息表征的精度。分别利用ROC曲线等评价指标对加入端元可变策略后探测器分离目标和背景效果、对背景端元估计偏差的敏感度等情况进行比较和分析,实验结果验证了端元可变策略的有效性,这也证明了通过提高表征背景精度的方式可以提高亚像元目标的探测效果。
(5)提出一种基于随机采样的异常目标探测方法,在缺乏先验目标信息的条件下,解决目标探测问题。该方法利用多个局域位置选取的像元作为背景信息,结合局域特征和全局特征,通过多次选取背景信息来建立不同的探测算子,最后融合多次探测结果,利用异常目标呈现异常次数最多的特点,确定异常目标。实验证明,该方法可以较好地抑制目标对背景信息的干扰,提高目标与背景的可分度。此外,还提出了一种实时探测的随机采样异常目标探测方法,实验证明其可以减少不同背景交界处的虚警数目。
Imaging spectrometer can provide images with very high spectral resolution by collecting many spectrally continuous images, named hyperspectral images, at the same time. Different materials are distinguishable by the spectral difference revealed in hyperspectral images. The spatial pattern of the images is also helpful to position the potential target with spectral difference from the background. These characteristics make hyperspectral images suitable for target detection task. However, due to the complex distribution of different ground objects and the limited spatial resolution of the hyperspectral images, a pixel in the hyperspectral images is usually composed of different land objects and the targets usually reside in the sub-pixel scale. Sub-pixle target detection is a difficulty for extracting information from hyperspectral images and has been of great interest to the researchers in target detection domain since the end of last century. This thesis focuses on the following problems in sub-pixel target detection from hyperspectral images:detection with low dimension data, target spectral variety, contamination caused by targets to the background statistics.
The main research work and the corresponding contributions of this dissertation are as following:
Target detection in hyperspectral images originates from the singal detection theory. So the singal processing theory is summarized. The signals received by the imaging spectrometer come from the array of multiple senors acquiring different radiances from the same ground scene. This is the array signal processing in hyperspectral images. Besides, the three most important methods for constructing the target detectors are detailed:generalized likelihood ratio test, constant false alarm rate detector and matched detector theory.
Linear mixture model is the most widely used model for target detection in hyperspectral images. The physical basis, the description and the limitation of the model are summarized. Based on the linear mixture model, the linear spectral unmixing based target detection method is introduced. The fully constrained least squares method is used to solve the problem and an active/passive sets based numerical calculation method is researched on. Experiments show the limitation of the method. Besides, a constrained energy minimization method based on minimum noise fraction transformation (MNF-CEM) is proposed. In this method, minimum noise fraction (MNF) reduces the dimension of the hyperspectral imageries and separates the noise from the hyperspectral images. Then a finite impulse response (FIR) filter is used to detect the sub-pixel targets in the low dimension images. In this way, the computations of ill-conditional matrix inverse and virtual dimension of the hyperspectral imageries are unnecessary. Experiments show that this method can restrain the influence of the noise and is an effective sub-pixel target detection method for hyperspectral imageries.
Subspace based methods can solve the spectral variety problem. Two subspace based detection methods are proposed in this paper. Traditional matched subspace detectors rely on the estimated endmembers and the according abundance to model the background and construct a generalized likelihood ratio test based detector. However, these endmembers and abundances have no physical meanings. So we use the abundances from the spectral unmixing so as to introduce the physically meaningful endmembers and abundances into the matched subspace detector. The proposed method is called spectral unmixing based matched subspace detector. The other method is the local OSP (Orthogonal Subspace Projection) method which uses the local neighbor pixels to represent the background information of the pixels under observation instead of the endmembers from the whole image. Experiments show that with physically meaningful endmembers and the according abundances the proposed matched subspace detector models the background better and performs better than its counterpart detector and the other OSP detectors; local OSP output a better result than OSP which proves that the pixel is related more with the local information than the whole set of pixels in the image, so it is more suitable to model the pixel's background with its neighboring pixels.
To further investigate the performance with more accurate background information, two hybrid detectors are proposed. Both of them make use of abundances from spectral unmixing, and a background endmembers selection procedure is introduced into the detectors. The aim of the background endmembers selection is to use the correct kinds of background endmembers in the detection, while traditional methods use the same endmembers in the detection of each pixel. A structured background hybrid detector and an unstructured background hybrid detector are developed. First, the endmembers selection procedure is performed to choose the correct kinds of background endmembers in each pixel. Second, spectral unmixing is done with the endmembers selection information to get more accurate abundances. Then, the abundances are used to construct the structured background hybrid detector and the unstructured background hybrid detector and the endmembers selection information is taken into consideration in the structured detector again. Experiments show that the hybrid detectors with endmembers selection procedure perform better than the hybrid ones without endmembers selection procedure. It is concluded that with more accurate information to model the reality of the background ground objects the detection performance would be improved.
Current anomaly detection methods are susceptiable to the anomalies in the background statistics. And they either use a local background statistics or a global statistics. A random selection based anomaly detector is proposed in this thesis. The method uses a random selection procedure to get background statistics from the image. Eachtime, not only the local neighboring pixles but also a number of blocks containting the same number of pixels are selected from random positions of the image. In this way, both local and global statistics of the pixel under observation are taken into consideration. The selection procedure is performed several times and each time the detection procedure is done successively with the constructed background statistics. Finally, the detection results are confused. Besides, the real-time version of the random selection based detector is implemented. Experiments show that the separability between targets and background by the proposed random selection based anomaly detector is larger than that by SSRX detector. And the performance of the real-time version detector also shows better detection and less false alarms on the boundaries of different backgrounds than current real-time anomaly detectors..
引文
* IEEE Signal Processing Magazine, Vol.19, No.1, Jan,2002.
[1]陈述彭,童庆禧,郭华东,遥感信息机理研究,北京：科学出版社,1998.
[2]褚海峰,翟中敏,赵银娣,李平湘,张良培,一种多/高光谱遥感图像端元提取的凸锥分析算法,遥感学报,2007,(11)4：460-467.
[3]杜博,钟燕飞,张良培,李平湘.一种自适应匹配子空间亚像元目标探测方法.遥感学报.2009a,13(4)：597-603.
[4]杜博,张良培,李平湘,钟燕飞.基于最小噪声分离的约束能量最小化亚像元目标探测方法.中国图象图形学报,2009b,14(9)：1850-1857.
[5]杜培军,方涛,唐宏,陈雍业.高光谱遥感信息中的特征提取与应用研究.光子学报,2005,(02)
[6]耿修瑞,高光谱遥感图像目标探测与分类技术研究[博士学位论文],中国科学院遥感应用研究所,2005.
[7]段凤增 编著,“信号检测理论”,哈尔滨,哈尔滨工业大学出版社,2002-1-1,第2版,pp.40-70。
[8]贺霖,潘泉,赵永强,等,基于子空间投影的未知背景航拍高光谱图像恒虚警率目标检测.航空学报,2006,27(4).
[9]贺霖,高光谱图像自动目标检测技术研究[博士学位论文],西北工业大学,2007
[10]承继成,郭华东,史文中等.遥感数据的不确定性研究.北京：科学出版社,2004.
[11]李智勇,高光谱图像异常检测方法研究[博士学位论文],国防科学技术大学,2004.
[12]路威,余旭初,刘娟,杨国鹏,基于分布异常的高光谱影像小目标检测算法,测绘学报,35(1),2006：40-45.
[13]吕长春,王忠武,钱少猛.混合像元分解模型综述.遥感信息.遥感信息,2003(3)：55-57.
[14]浦瑞良,宫鹏,高光谱遥感及其应用.北京：高等教育出版社,2003.
[15]田庆久,宫鹏,地物波谱数据库研究现状与发展趋势.遥感信息,2002,(3)：
1-6.
[16]童庆禧,张兵,郑兰芬,高光谱遥感：原理、技术与应用,高等教育出版社,2006a.
[17]童庆禧,张兵,郑兰芬,高光谱遥感的多学科应用.北京：电子工业出版社,2006b.
[18]吴波,张良培,李平湘.高光谱端元自动提取的迭代分解方法,遥感学报,2005,9(3)：286-293.
[19]吴波。混合像元自动分解及其扩展模型研究[博士学位论文论文],武汉大学,2006.
[20]吴柯,李平湘,张良培,一种端元变化的神经网络混合像元分解方法,遥感学报,11(1),2007：20-26.
[21]徐希孺等。1990,混合像元的因子分析方法及其在大面积冬小麦面积估算中的应用,科学通报,35(4)；317-320
[22]张良培,利用高光谱对生物量进行估计.遥感学报,1997.
[23]张良培,光谱分析在高光谱遥感中的应用[博士学位论文],武汉大学,1998.
[24]张良培,张立福,高光谱遥感,武汉：武汉大学出版社,2005.
[25]张良培,黄昕,遥感影像信息处理技术的研究进展[J].遥感学报,2009,(04).
[26]张兵,陈正超,郑兰芬等.基于高光谱图像特征提取与凸面几何体投影变换的目标探测.红外与毫米波学报,2004,23(6)：441-445.
[27]赵英时,遥感应用分析原理与方法,北京：科学出版社,2003.
[28]郑兰芬,王晋年,成像光谱遥感技术及其图像光谱信息提取的分析研究.遥感学报,1992,(1)：49-60.
[29]Baker C. J., Pink, J.M. Tough, R.J.A. A statistical model for radar target detection in clutter.Radar Conference,1998. Proceedings of the 1988 IEEE National April 1988:241-245
[30]Becker C. and Gather U., The Masking Breakdown Point of Multivariate Outlier Identification Rules, Journal of American Statistical Association,94,1999: 947-955.
[31]Billor N., Hadi A. S., and Velleman P. F., BACON:blocked adaptive computationally efficient outlier nominators, Computational Statistics and Data Analysis,43(2),2005 388-397.
[32]Bradley A. P., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition,7(30),1997:1145-1159.
[33]Broadwater J. and Chellappa R., "Hybrid Detectors for Subpixel Targets," IEEE Trans. Pattern Anal. Mach. Intell.,29(11),2007:1891-1903.
[34]Caefer C. E., Rotman S. R., Silverman J., et al. Algorithms for Point Target Detection in Hyperspectral Imagery. Proceedings of the SPIE Imaging Spectrometry Ⅷ, Vol.4816,2002:242-257.
[35]Casella G. and Berger R.L., Statistical Inference, Second Ed. Duxbury Press, 2001.
[36]Chang C. I., Du Q., Noise Subspace Projection Approach to Target Signature Detection and Extraction in an Unknown Background for Hyperspectral Images, IEEE Transaction on Geoscience and Remote Sensing,36(1),1999:171-181.
[37]Chang C.-I. and Heinz D. C., Constrained Subpixel Target Detection for Remotely Sensed Imagery," IEEE Trans. Geosci. Remote Sens.,38(3),2000: 1144-1159.
[38]Chang Chein-I, Chiang Shao-Shan, Anomaly detection and classification for hyperspectral imagery. Trans. Geosci. Remote Sensing,40(6) 2002:1314-1325
[39]Chang C.-I., Hyperspectral Imaging:Techniques for Spectral Detection and Classification. New York:Kluwer,2003a.
[40]Chang C. I., Hyperspectral Imaging:Techniques for Spectral Detection and Classification, Kluwer Academic,2003.
[41]Chang C.-I and Du Q., Estimation of number of spectrally distinct signal sources in hyperspectral imagery, IEEE Trans. Geosci. Remote Sens.,42(3),2004: 608-619.
[42]Chang C.-I., Exploration of Virtual Dimensionality in Hyperspectral Image Analysis. Proceedings of the SPIE,2006,6233:623313.
[43]Chiang S S, CHANG C I, GINSBERG I. W., Unsupervised Target Detection in Hyperspectral Images Using Projection Pursuit, IEEE Trans. Geosci. Remote Sensing,2001,39(7):1314-1325.
[44]Chapple P.B., Bertilone D.C, Caprari R.S, Newsam G.N., Stochastic model-based processing for detection of small targets in non-Gaussian natural imagery. IEEE Transactions on Image Processing,10(4),2001:554-564
[45]Chiang L. H., Pell R. J., and Seasholtz M. B., Exploring Process Data with the Use of Robust Outlier Detection Algorithms, Journal of Process Control,13(5), 2003:437-449.
[46]Datt B., Identification of green and dry vegetation components with a cross-correlogram spectral matching, Int. J. Remote Sens.,21(10),2000: 2133-2139.
[47]Dennison P. E. and Roberts D. A., The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral. Remote Sensing of Environment,87, March,2003a:295-309
[48]Dennison P. E. and Roberts D. A., Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sensing of Environment,87,2003b:123-135.
[49]Du, B., Zhang, L., Li, P.,Zhong, Y, A Target Detection Algorithm for Urban Areas Using High Spatial Resolution Hyperspectral Remote Sensing Imagery, in Joint Urban Remote Sensing Event 2009.
[50]Du Q., Chang C. I., Interference Subspace Projection Approach to Subpixel Target detection. SPIE Proceedings of Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery Ⅶ, Vol.4381,2001:570-577.
[51]Du Qian, Chang C. I., A Signal-Decomposed and Interference-Annihilated Approach to Hyperspectral Target Detection, IEEE Transactions on Geoscience and Remote Sensing,42(4),2004:892-906.
[52]Ferrier G., "Application of Imaging Spectrometer Data in Identifying Environmental Pollution Caused by Mining at Rodaquilar, Spain," Int. J. Remote Sens., vol.68, no.2, pp.125-137,1999.
[53]Farrand W. H. and Harsanyi J. C., Discrimination of poorly exposed lithologies in imaging spectrometer data, J. Geophys. Res., vol.100, Jan.1995:1565-1578.
[54]Filippi A. M. and Archibald R., Support Vector Machine-Based Endmember Extraction, IEEE Trans. Geosci. Remote Sens., vol.47, no.3, pp.771-791, Mar. 2009.
[55]Gaucel J.-M., Guillaume M., Bourennane S., Whitening Spatial Correlation Filtering For Hyperspectral Anomaly Detector, ICASSP IEEE Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.5,2005:333-336.
[56]Green A., Berman M., Switzer P., Craig M. D, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing,1988,26(1),1998: 65-74.
[57]Hapke B. Bidirectional reflectance spectroscopy theory. Geophysical research.1981,86(B4):3039-3054.
[58]Heinz D. C., Chang C. I., Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing,39(3),2001:529-545.
[59]Heinz D., Chang C. I., Althouse M. L. G., Fully constrained least squares-based linear unmixing, in Geoscience and Remote Sensing Symp.'99, Hamburg, Germany,1999.
[60]Horig B., Oschutz F., Lehmann F. and Kuhn F., "Hymap hyperspectral remote sensing to detect hydrocarbons," International Journal of Remote Sensing,22(8), 2001:1413-1422.
[61]Hunt G. R., Electromagnetic radiation:the communication link in remote sensing[A]. Remote Sensing in Geology[M]. Siegal B., Gillespia A. Wiley New York,1980:5-45.
[62]Jiao Xiaoli and Chang Chein-I, Kernel-Based Constrained Energy Minimization, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery ⅩⅣ, Proc. of SPIE Vol.6966,2008:69661S1-11.
[63]Johnson P.E., Smith M.O., Taylor-George S. and Adams J.B., A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures, J Geophys Res 88 1983:3557-3561.
[64]John P. Kerekes, Jerrold E. Baum, Hyperspectral Imaging System Modeling, Lincoln Laboratory Journal,14(1),2003:29-54.
[65]Kay S.M., Fundamentals of Statistical Signal Processing. Englewood Cliffs, NJ: Prentice Hall,1998.
[66]Kelly E. J., An adaptive detection algorithm, IEEE Trans. Aerosp. Electron. Syst., vol.22(2),1986:115-127.
[67]Kelly E. J., Adaptive detection in non-stationary interference, part III, MIT Lincoln Laboratory, Lexington, MA, Tech. Rep.761,1987.
[68]Keshava N., Kerekes J., Manolakis D., and Shaw G., An algorithm taxonomy for hyperspectral unmixing, Proc. SPIE,2000:42-63,.
[69]Kratt Chris, Calvin Wendy and Coolbaugh Mark, "Geothermal exploration with Hymap hyperspectral data at Brady-Desert Peak, Nevada," Remote Sensing of Environment, vol.104, no.3, pp.313-324, May 2006.
[70]Krout Shawn, Scharf L. L., Todd, L., McWhorter, Todd, Adaptive Subspace Detectors, IEEE Transactions on Signal Processing,49(1),2001:1-16.
[71]Kwon Heesung, Nasrabadi N. M., Kernel RX-Algorithm:a nonlinear anomaly detector for hyperspectral imagery. IEEE Trans. Geosci. Remote Sensing,43(2), 2005:388-397
[72]Kwon H., Der S. Z., Narabadi N. M., Adaptive Anomaly Detection Using Subspace Separation for Hyperspectral Imagery, Optical Engineering,42(11), 2003a:3342-3351.
[73]Kwon H., Der S. Z., Narabadi N. M., Projection-based Adaptive Anomaly Detection for Hyperspectral Imagery, Proceedings of 2003 International Conference on Image Processing, Vol.1,2003:1001-1004.
[74]Kwon H., Narabadi N. M., Hyperspectral Target Detection Using Kernel Matched Subspace Detector. Proceedings of 2004 International Conference on Image Processing,2004a:3327-3330.
[75]Kwon H., Narabadi N. M., Kernel-Based Subpixel Target Detection in Hyperspectral Images, Proceedings of IEEE International Joint Conference on Neural Network,2004b:717-721.
[76]Kwon H., Narabadi N. M., Kernel RX-Algorithm:A Nonlinear Anomaly Detector for Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing,43(2),2005:388-397.
[77]Landgrebe D., Hyperspectral image data analysis, IEEE Signal Processing Magazine,19(1),2002:17-28.
[78]Lee J B, Woodyatt A S, Berman M. Enhancement of High Specttral Resolution Remote Sensing Data by a Noise-adjusted Principal Components Transform. IEEE Transactions on Geoscience and Remote Sensing,1990,28 (3):295-304.
[79]Li Hongbin, Michels J. H., Parametric Adaptive Signal Detection for Hyperspectral Imaging, IEEE Transactions on Signal Processing,54(7),2006: 2704-2715.Goetz A., Vane G. Imaging Spectrometry of Earth Remote Sensing, Science,228(4704),1985:1147.
[80]Michael D. Farrell Jr., Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery, PHD Dissertation Georgia Institute of Technology,2005.
[81]Manolakis D., Shaw G., and Keshava N., Comparative analysis of hyperspectral adaptive matched filter detectors, Proc. SPIE, vol.4049,2000:2-17.
[82]Manolakis D.G., Marden D., Kerekes J.P., Shaw G.A., On the statistics of hyperspectral imaging data. Proceedings of SPIE vol.4381,2001:308-316
[83]Manolakis D., Siracusa C., and Shaw G., Adaptive matched subspace detectors for hyperspectral imaging applications, in IEEE Int. Conf.Acoustics, Speech, and Signal Processing, Salt Lake City, UT, May 2001.
[84]Manolakis D., Siracusa C., Marden D., Shaw G., Hyperspectral Adaptive Matched Filter Detectors:Practical Performance Comparison. SPIE Proceedings of Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery Ⅶ, Vol.4381,2001:18-33.
[85]Manolakis D., Shaw, G., Detection Algorithms for Hyperspectral Imaging Application, IEEE Signal Processing Magazine,19(1),2002:29-43.
[86]Manolakis D., Detection Algorithms for Hyperspectral Imaging Applications:A Signal Processing Perspective, IEEE Proceedings of Workshop on Advances in
Techniques for Analysis of Remotely Sensed Data,2003a:378-384.
[87]Manolakis D., Marden D., and Shaw G. A., "Hyperspectral image processing for automatic target detection applications," Lincoln Lab. J.,14(1),2003b:79-116.
[88]Manolakis D., Taxonomy of Detection Algorithms for Hyperspectral Imaging Applications, Optical Engineering,44(6),2005:1-11.
[89]Martinez P. J., Perez R. M., Plaza A., Aguilar P. L., Cantero M. C., and Plaza J., Endmember extraction algorithms from hyperspectral images, Annals of Geophysics,49(1),2006:93-101.
[90]Margalit A., Reed I. S., Gagliardi R. M., Adaptive optical target detection using correlated images. IEEE Transactions on aerospace and electronic systems.21(3), 1985:394-405
[91]Maselli F., "Multiclass spectral decomposition of remotely sensed scenes by selective pixel unmixing," IEEE Trans. Geosci. Remote Sens.,36(5),1998: 1809-1820.
[92]Meer V. F. and Bakker W., "Cross correlogram spectral matching (CCSM): application to surface mineralogical mapping using AVIRIS data from Cuprite, Nevada," Remote Sensing of Environment,61(3),1997:371-382.
[93]Mustard, J. F., and Pieters C. M., Abundance and distribution of ultramafic microbreccia in Moses Rock Dike:Quantitative application of mapping spectroscopy, J. Geophys. Res.,92,1987:10376-10390.
[94]Narabadi N. M., Kwon H., Kernel Spectral Matched Filter for Hyperspectral Target Detection, Proceedings of 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing 2005:681-684.
[95]Neville R. A., Staenz K., Szeredi T., Lefebvre J., and Hauff P., Automatic Endmember Extraction from Hyperspectral Data for Mineral Exploration, in Proceedings of Fourth International Airborne Remote Sensing Conference and Exhibition/21st Canadian Symposium on Remote Sensing, vol.2, Ottawa, Canada, pp.891-897,1999.
[96]Novak L.M., "Radar target detection and map-matching algorithm studies," IEEE Transactions on Aerospace and Electronic Systems, vol. AES-16, pp.620, Sept. 1980.
[97]Plaza A., Martinez P., Perez R., et al., A New Method for Target Detection in Hyperspectral Imagery Based on Extended Morphological Profiles, Proceedings of IEEE International Conference on Geoscience and Remote Sensing, Vol.6, 2003:3773-3774.
[98]Plaza A., Martinez P., Plaza J., et al., Dimensionality Reduction and Classification of Hyperspectral Image Data Using Sequences of Extended Morphological Transformations, IEEE Transactions on Geoscience and Remote Sensing,43(3),2005:466-479.
[99]Romano J. M., and Rosario D., Random sampling statistical analysis for adaptive target-scale-invariant hyperspectral anomaly detection, Proc. SPIE,6565,2007: 656522-1-9.
[100]Reed I.S., Mallett J.D., and Brennan L.E., "Rapid Convergence Rate in Adaptive Arrays," IEEE Trans. Aerospace and Electronic Systems,10,1974: 853-863.
[101]Reed Irving S, Yu Xiaoli, Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution, IEEE Transactions on Acoustics, Speech and Signal Processing,38(10),1990:1760-1770.
[102]Reed R., Yu X.. Adaptive multi-band CFAR detection of an optical pattern with unknown spectral distribution. Trans. Acoust., Speech, Singal Process, 38(10),1990:293-305
[103]Richard T. B., Louis L. S., Signal Processing Applications of Oblique Projection Operators. IEEE Transactions on Signal Processing,42(6),1994 1413-1424.
[104]Ren H. and Chang C.-I, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral imagery," Opt.Eng., vol. 39(12),2000:3138-3145.
[105]Ren Hsuan, Du Qian, Jensen James, Efficient Anomaly Detection and Discrimination for Hyperspectral Imagery. Proceedings of SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral
Imagery Ⅷ, Vol.4725,2002:234-241.
[106]Roberts D. A., Gardner. M., Church R., Ustin S., Scheer G., and Green R.O., Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sensing of Environment,65,1998:267-279.
[107]CCSM:Cross correlogram spectral matching, Int. J. Remote Sens.,18(5), 1997:1197-1201,.
[108]Robila S. A., Varshney P. K., Target Detection in Hyperspectral Images based on Independent Component Analysis, Proceedings of SPIE Conference on Automatic Target Recognition Ⅻ, Vol.4726,2002:173-183.
[109]Rohling H., Radar CFAR Thresholding in Clutter and Multiple Target Situations, IEEE Transactions on Aerospace and Electronic Systems,19(4),1983: 608-621.
[110]Sabol D. E., Adams J. B., Smith M. O., Quantitative sub-pixel spectral detection of targets in multispectral images, J. Geophys. Res.,97,1992: 2659-2672.
[111]Schweizer S. M. and Moura J. M. F, Hyperspectral imagery:clutter adaptation in anomaly detection. IEEE Trans. Information Theory 46(5),2000: 1855-1871
[112]Settle J. J., Drake N. A., Linear mixing and estimation of ground cover proportions, Int. J. Remote Sensing,14(6),1993:1159-1177.
[113]Smith D.B. and Johnson K.S., Single-step purification of polypeptides expressed in Escheric hia coli as fusion protein with glutathione-s-transferase, Gene 67 1988:31-40
[114]Smith M. O., Sabol D. E., Adams J. B., Spectral mixture analysis-new strategies for the analysis of multispectral
[115]Smith M. O., Johnson P. E., and Adams J. B., "Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis," J. Geophys. Res., vol.90, February 1985:797-804.
[116]Stein D. W. J., Beaven S. G., Hoff L. E., Winter E. M., Schaum A. P., Stocker A. D., Anomaly Detection from Hyperspectral Imagery, IEEE Signal Processing Magazine,19(1),2002:58-69.
[117]Thai B. and Healey G., Invariant subpixel target identification in hyperspectral imagery, Proc. SPIE,1999.
[118]Tompkins S., Mustard J. F., Pieters C. M., and Forsyth D. W., Optimization of endmembers for spectral mixture analysis, Remote sensing of environment, 59(3),1997:472-489.
[119]Tu T. M., Chen C. H., Chang C. I., A Posteriori Least Squares Orthogonal Subspace Projection Approach to Desired Signature Extraction and Detection, IEEE Transactions on Geoscience and Remote Sensing, Vol.35(1), 1997:127-139.
[120]Tu T. M., Chen C. H., Chang C. I., A Noise Subspace Projection Approach to Target Signature Detection and Extraction in an Unknown Background for hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, 36(1),1999:171-181.
[121]Wu K., Niu R., Zhang L., and Li P., A neural network method of selective endmember for pixel unmixing, in 27th Asian Conference on Remote Sensing (ACRS), Oct.,2006.
[122]Yu Xiaoli, Reed Irving S., Alan D. Stocker, Comparative performance Analysis of Adaptive Multispectral Detectors, IEEE Transactions on Signal Processing,41(8),1993:2639-2656.
[123]Zhang L., Du Bo, Zhong Y., Hybrid Detectors Based on Selective Endmembers, IEEE Transactions on Geoscience and Remote Sensing,2010, DOI: 10.1109/TGRS.2010.2040284.
[1]陈述彭,童庆禧,郭华东,遥感信息机理研究,北京：科学出版社,1998.
[2]褚海峰,翟中敏,赵银娣,李平湘,张良培,一种多/高光谱遥感图像端元提取的凸锥分析算法,遥感学报,2007,(11)4：460-467.
[3]杜博,钟燕飞,张良培,李平湘.一种自适应匹配子空间亚像元目标探测方法.遥感学报.2009a,13(4)：597-603.
[4]杜博,张良培,李平湘,钟燕飞.基于最小噪声分离的约束能量最小化亚像元目标探测方法.中国图象图形学报,2009b,14(9)：1850-1857.
[5]杜培军,方涛,唐宏,陈雍业.高光谱遥感信息中的特征提取与应用研究.光子学报,2005,(02)
[6]耿修瑞,高光谱遥感图像目标探测与分类技术研究[博士学位论文],中国科学院遥感应用研究所,2005.
[7]段凤增 编著,“信号检测理论”,哈尔滨,哈尔滨工业大学出版社,2002-1-1,第2版,pp.40-70。
[8]贺霖,潘泉,赵永强,等,基于子空间投影的未知背景航拍高光谱图像恒虚警率目标检测.航空学报,2006,27(4).
[9]贺霖,高光谱图像自动目标检测技术研究[博士学位论文],西北工业大学,2007
[10]承继成,郭华东,史文中等.遥感数据的不确定性研究.北京：科学出版社,2004.
[11]李智勇,高光谱图像异常检测方法研究[博士学位论文],国防科学技术大学,2004.
[12]路威,余旭初,刘娟,杨国鹏,基于分布异常的高光谱影像小目标检测算法,测绘学报,35(1),2006：40-45.
[13]吕长春,王忠武,钱少猛.混合像元分解模型综述.遥感信息.遥感信息,2003(3)：55-57.
[14]浦瑞良,宫鹏,高光谱遥感及其应用.北京：高等教育出版社,2003.
[15]田庆久,宫鹏,地物波谱数据库研究现状与发展趋势.遥感信息,2002,(3)：
1-6.
[16]童庆禧,张兵,郑兰芬,高光谱遥感：原理、技术与应用,高等教育出版社,2006a.
[17]童庆禧,张兵,郑兰芬,高光谱遥感的多学科应用.北京：电子工业出版社,2006b.
[18]吴波,张良培,李平湘.高光谱端元自动提取的迭代分解方法,遥感学报,2005,9(3)：286-293.
[19]吴波。混合像元自动分解及其扩展模型研究[博士学位论文论文],武汉大学,2006.
[20]吴柯,李平湘,张良培,一种端元变化的神经网络混合像元分解方法,遥感学报,11(1),2007：20-26.
[21]徐希孺等。1990,混合像元的因子分析方法及其在大面积冬小麦面积估算中的应用,科学通报,35(4)；317-320
[22]张良培,利用高光谱对生物量进行估计.遥感学报,1997.
[23]张良培,光谱分析在高光谱遥感中的应用[博士学位论文],武汉大学,1998.
[24]张良培,张立福,高光谱遥感,武汉：武汉大学出版社,2005.
[25]张良培,黄昕,遥感影像信息处理技术的研究进展[J].遥感学报,2009,(04).
[26]张兵,陈正超,郑兰芬等.基于高光谱图像特征提取与凸面几何体投影变换的目标探测.红外与毫米波学报,2004,23(6)：441-445.
[27]赵英时,遥感应用分析原理与方法,北京：科学出版社,2003.
[28]郑兰芬,王晋年,成像光谱遥感技术及其图像光谱信息提取的分析研究.遥感学报,1992,(1)：49-60.
[29]Baker C. J., Pink, J.M. Tough, R.J.A. A statistical model for radar target detection in clutter.Radar Conference,1998. Proceedings of the 1988 IEEE National April 1988:241-245
[30]Becker C. and Gather U., The Masking Breakdown Point of Multivariate Outlier Identification Rules, Journal of American Statistical Association,94,1999: 947-955.
[31]Billor N., Hadi A. S., and Velleman P. F., BACON:blocked adaptive computationally efficient outlier nominators, Computational Statistics and Data Analysis,43(2),2005 388-397.
[32]Bradley A. P., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition,7(30),1997:1145-1159.
[33]Broadwater J. and Chellappa R., "Hybrid Detectors for Subpixel Targets," IEEE Trans. Pattern Anal. Mach. Intell.,29(11),2007:1891-1903.
[34]Caefer C. E., Rotman S. R., Silverman J., et al. Algorithms for Point Target Detection in Hyperspectral Imagery. Proceedings of the SPIE Imaging Spectrometry Ⅷ, Vol.4816,2002:242-257.
[35]Casella G. and Berger R.L., Statistical Inference, Second Ed. Duxbury Press, 2001.
[36]Chang C. I., Du Q., Noise Subspace Projection Approach to Target Signature Detection and Extraction in an Unknown Background for Hyperspectral Images, IEEE Transaction on Geoscience and Remote Sensing,36(1),1999:171-181.
[37]Chang C.-I. and Heinz D. C., Constrained Subpixel Target Detection for Remotely Sensed Imagery," IEEE Trans. Geosci. Remote Sens.,38(3),2000: 1144-1159.
[38]Chang Chein-I, Chiang Shao-Shan, Anomaly detection and classification for hyperspectral imagery. Trans. Geosci. Remote Sensing,40(6) 2002:1314-1325
[39]Chang C.-I., Hyperspectral Imaging:Techniques for Spectral Detection and Classification. New York:Kluwer,2003a.
[40]Chang C. I., Hyperspectral Imaging:Techniques for Spectral Detection and Classification, Kluwer Academic,2003.
[41]Chang C.-I and Du Q., Estimation of number of spectrally distinct signal sources in hyperspectral imagery, IEEE Trans. Geosci. Remote Sens.,42(3),2004: 608-619.
[42]Chang C.-I., Exploration of Virtual Dimensionality in Hyperspectral Image Analysis. Proceedings of the SPIE,2006,6233:623313.
[43]Chiang S S, CHANG C I, GINSBERG I. W., Unsupervised Target Detection in Hyperspectral Images Using Projection Pursuit, IEEE Trans. Geosci. Remote Sensing,2001,39(7):1314-1325.
[44]Chapple P.B., Bertilone D.C, Caprari R.S, Newsam G.N., Stochastic model-based processing for detection of small targets in non-Gaussian natural imagery. IEEE Transactions on Image Processing,10(4),2001:554-564
[45]Chiang L. H., Pell R. J., and Seasholtz M. B., Exploring Process Data with the Use of Robust Outlier Detection Algorithms, Journal of Process Control,13(5), 2003:437-449.
[46]Datt B., Identification of green and dry vegetation components with a cross-correlogram spectral matching, Int. J. Remote Sens.,21(10),2000: 2133-2139.
[47]Dennison P. E. and Roberts D. A., The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral. Remote Sensing of Environment,87, March,2003a:295-309
[48]Dennison P. E. and Roberts D. A., Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sensing of Environment,87,2003b:123-135.
[49]Du, B., Zhang, L., Li, P.,Zhong, Y, A Target Detection Algorithm for Urban Areas Using High Spatial Resolution Hyperspectral Remote Sensing Imagery, in Joint Urban Remote Sensing Event 2009.
[50]Du Q., Chang C. I., Interference Subspace Projection Approach to Subpixel Target detection. SPIE Proceedings of Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery Ⅶ, Vol.4381,2001:570-577.
[51]Du Qian, Chang C. I., A Signal-Decomposed and Interference-Annihilated Approach to Hyperspectral Target Detection, IEEE Transactions on Geoscience and Remote Sensing,42(4),2004:892-906.
[52]Ferrier G., "Application of Imaging Spectrometer Data in Identifying Environmental Pollution Caused by Mining at Rodaquilar, Spain," Int. J. Remote Sens., vol.68, no.2, pp.125-137,1999.
[53]Farrand W. H. and Harsanyi J. C., Discrimination of poorly exposed lithologies in imaging spectrometer data, J. Geophys. Res., vol.100, Jan.1995:1565-1578.
[54]Filippi A. M. and Archibald R., Support Vector Machine-Based Endmember Extraction, IEEE Trans. Geosci. Remote Sens., vol.47, no.3, pp.771-791, Mar. 2009.
[55]Gaucel J.-M., Guillaume M., Bourennane S., Whitening Spatial Correlation Filtering For Hyperspectral Anomaly Detector, ICASSP IEEE Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.5,2005:333-336.
[56]Green A., Berman M., Switzer P., Craig M. D, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing,1988,26(1),1998: 65-74.
[57]Hapke B. Bidirectional reflectance spectroscopy theory. Geophysical research.1981,86(B4):3039-3054.
[58]Heinz D. C., Chang C. I., Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing,39(3),2001:529-545.
[59]Heinz D., Chang C. I., Althouse M. L. G., Fully constrained least squares-based linear unmixing, in Geoscience and Remote Sensing Symp.'99, Hamburg, Germany,1999.
[60]Horig B., Oschutz F., Lehmann F. and Kuhn F., "Hymap hyperspectral remote sensing to detect hydrocarbons," International Journal of Remote Sensing,22(8), 2001:1413-1422.
[61]Hunt G. R., Electromagnetic radiation:the communication link in remote sensing[A]. Remote Sensing in Geology[M]. Siegal B., Gillespia A. Wiley New York,1980:5-45.
[62]Jiao Xiaoli and Chang Chein-I, Kernel-Based Constrained Energy Minimization, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery ⅩⅣ, Proc. of SPIE Vol.6966,2008:69661S1-11.
[63]Johnson P.E., Smith M.O., Taylor-George S. and Adams J.B., A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures, J Geophys Res 88 1983:3557-3561.
[64]John P. Kerekes, Jerrold E. Baum, Hyperspectral Imaging System Modeling, Lincoln Laboratory Journal,14(1),2003:29-54.
[65]Kay S.M., Fundamentals of Statistical Signal Processing. Englewood Cliffs, NJ: Prentice Hall,1998.
[66]Kelly E. J., An adaptive detection algorithm, IEEE Trans. Aerosp. Electron. Syst., vol.22(2),1986:115-127.
[67]Kelly E. J., Adaptive detection in non-stationary interference, part III, MIT Lincoln Laboratory, Lexington, MA, Tech. Rep.761,1987.
[68]Keshava N., Kerekes J., Manolakis D., and Shaw G., An algorithm taxonomy for hyperspectral unmixing, Proc. SPIE,2000:42-63,.
[69]Kratt Chris, Calvin Wendy and Coolbaugh Mark, "Geothermal exploration with Hymap hyperspectral data at Brady-Desert Peak, Nevada," Remote Sensing of Environment, vol.104, no.3, pp.313-324, May 2006.
[70]Krout Shawn, Scharf L. L., Todd, L., McWhorter, Todd, Adaptive Subspace Detectors, IEEE Transactions on Signal Processing,49(1),2001:1-16.
[71]Kwon Heesung, Nasrabadi N. M., Kernel RX-Algorithm:a nonlinear anomaly detector for hyperspectral imagery. IEEE Trans. Geosci. Remote Sensing,43(2), 2005:388-397
[72]Kwon H., Der S. Z., Narabadi N. M., Adaptive Anomaly Detection Using Subspace Separation for Hyperspectral Imagery, Optical Engineering,42(11), 2003a:3342-3351.
[73]Kwon H., Der S. Z., Narabadi N. M., Projection-based Adaptive Anomaly Detection for Hyperspectral Imagery, Proceedings of 2003 International Conference on Image Processing, Vol.1,2003:1001-1004.
[74]Kwon H., Narabadi N. M., Hyperspectral Target Detection Using Kernel Matched Subspace Detector. Proceedings of 2004 International Conference on Image Processing,2004a:3327-3330.
[75]Kwon H., Narabadi N. M., Kernel-Based Subpixel Target Detection in Hyperspectral Images, Proceedings of IEEE International Joint Conference on Neural Network,2004b:717-721.
[76]Kwon H., Narabadi N. M., Kernel RX-Algorithm:A Nonlinear Anomaly Detector for Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing,43(2),2005:388-397.
[77]Landgrebe D., Hyperspectral image data analysis, IEEE Signal Processing Magazine,19(1),2002:17-28.
[78]Lee J B, Woodyatt A S, Berman M. Enhancement of High Specttral Resolution Remote Sensing Data by a Noise-adjusted Principal Components Transform. IEEE Transactions on Geoscience and Remote Sensing,1990,28 (3):295-304.
[79]Li Hongbin, Michels J. H., Parametric Adaptive Signal Detection for Hyperspectral Imaging, IEEE Transactions on Signal Processing,54(7),2006: 2704-2715.Goetz A., Vane G. Imaging Spectrometry of Earth Remote Sensing, Science,228(4704),1985:1147.
[80]Michael D. Farrell Jr., Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery, PHD Dissertation Georgia Institute of Technology,2005.
[81]Manolakis D., Shaw G., and Keshava N., Comparative analysis of hyperspectral adaptive matched filter detectors, Proc. SPIE, vol.4049,2000:2-17.
[82]Manolakis D.G., Marden D., Kerekes J.P., Shaw G.A., On the statistics of hyperspectral imaging data. Proceedings of SPIE vol.4381,2001:308-316
[83]Manolakis D., Siracusa C., and Shaw G., Adaptive matched subspace detectors for hyperspectral imaging applications, in IEEE Int. Conf.Acoustics, Speech, and Signal Processing, Salt Lake City, UT, May 2001.
[84]Manolakis D., Siracusa C., Marden D., Shaw G., Hyperspectral Adaptive Matched Filter Detectors:Practical Performance Comparison. SPIE Proceedings of Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery Ⅶ, Vol.4381,2001:18-33.
[85]Manolakis D., Shaw, G., Detection Algorithms for Hyperspectral Imaging Application, IEEE Signal Processing Magazine,19(1),2002:29-43.
[86]Manolakis D., Detection Algorithms for Hyperspectral Imaging Applications:A Signal Processing Perspective, IEEE Proceedings of Workshop on Advances in
Techniques for Analysis of Remotely Sensed Data,2003a:378-384.
[87]Manolakis D., Marden D., and Shaw G. A., "Hyperspectral image processing for automatic target detection applications," Lincoln Lab. J.,14(1),2003b:79-116.
[88]Manolakis D., Taxonomy of Detection Algorithms for Hyperspectral Imaging Applications, Optical Engineering,44(6),2005:1-11.
[89]Martinez P. J., Perez R. M., Plaza A., Aguilar P. L., Cantero M. C., and Plaza J., Endmember extraction algorithms from hyperspectral images, Annals of Geophysics,49(1),2006:93-101.
[90]Margalit A., Reed I. S., Gagliardi R. M., Adaptive optical target detection using correlated images. IEEE Transactions on aerospace and electronic systems.21(3), 1985:394-405
[91]Maselli F., "Multiclass spectral decomposition of remotely sensed scenes by selective pixel unmixing," IEEE Trans. Geosci. Remote Sens.,36(5),1998: 1809-1820.
[92]Meer V. F. and Bakker W., "Cross correlogram spectral matching (CCSM): application to surface mineralogical mapping using AVIRIS data from Cuprite, Nevada," Remote Sensing of Environment,61(3),1997:371-382.
[93]Mustard, J. F., and Pieters C. M., Abundance and distribution of ultramafic microbreccia in Moses Rock Dike:Quantitative application of mapping spectroscopy, J. Geophys. Res.,92,1987:10376-10390.
[94]Narabadi N. M., Kwon H., Kernel Spectral Matched Filter for Hyperspectral Target Detection, Proceedings of 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing 2005:681-684.
[95]Neville R. A., Staenz K., Szeredi T., Lefebvre J., and Hauff P., Automatic Endmember Extraction from Hyperspectral Data for Mineral Exploration, in Proceedings of Fourth International Airborne Remote Sensing Conference and Exhibition/21st Canadian Symposium on Remote Sensing, vol.2, Ottawa, Canada, pp.891-897,1999.
[96]Novak L.M., "Radar target detection and map-matching algorithm studies," IEEE Transactions on Aerospace and Electronic Systems, vol. AES-16, pp.620, Sept. 1980.
[97]Plaza A., Martinez P., Perez R., et al., A New Method for Target Detection in Hyperspectral Imagery Based on Extended Morphological Profiles, Proceedings of IEEE International Conference on Geoscience and Remote Sensing, Vol.6, 2003:3773-3774.
[98]Plaza A., Martinez P., Plaza J., et al., Dimensionality Reduction and Classification of Hyperspectral Image Data Using Sequences of Extended Morphological Transformations, IEEE Transactions on Geoscience and Remote Sensing,43(3),2005:466-479.
[99]Romano J. M., and Rosario D., Random sampling statistical analysis for adaptive target-scale-invariant hyperspectral anomaly detection, Proc. SPIE,6565,2007: 656522-1-9.
[100]Reed I.S., Mallett J.D., and Brennan L.E., "Rapid Convergence Rate in Adaptive Arrays," IEEE Trans. Aerospace and Electronic Systems,10,1974: 853-863.
[101]Reed Irving S, Yu Xiaoli, Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution, IEEE Transactions on Acoustics, Speech and Signal Processing,38(10),1990:1760-1770.
[102]Reed R., Yu X.. Adaptive multi-band CFAR detection of an optical pattern with unknown spectral distribution. Trans. Acoust., Speech, Singal Process, 38(10),1990:293-305
[103]Richard T. B., Louis L. S., Signal Processing Applications of Oblique Projection Operators. IEEE Transactions on Signal Processing,42(6),1994 1413-1424.
[104]Ren H. and Chang C.-I, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral imagery," Opt.Eng., vol. 39(12),2000:3138-3145.
[105]Ren Hsuan, Du Qian, Jensen James, Efficient Anomaly Detection and Discrimination for Hyperspectral Imagery. Proceedings of SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral
Imagery Ⅷ, Vol.4725,2002:234-241.
[106]Roberts D. A., Gardner. M., Church R., Ustin S., Scheer G., and Green R.O., Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sensing of Environment,65,1998:267-279.
[107]CCSM:Cross correlogram spectral matching, Int. J. Remote Sens.,18(5), 1997:1197-1201,.
[108]Robila S. A., Varshney P. K., Target Detection in Hyperspectral Images based on Independent Component Analysis, Proceedings of SPIE Conference on Automatic Target Recognition Ⅻ, Vol.4726,2002:173-183.
[109]Rohling H., Radar CFAR Thresholding in Clutter and Multiple Target Situations, IEEE Transactions on Aerospace and Electronic Systems,19(4),1983: 608-621.
[110]Sabol D. E., Adams J. B., Smith M. O., Quantitative sub-pixel spectral detection of targets in multispectral images, J. Geophys. Res.,97,1992: 2659-2672.
[111]Schweizer S. M. and Moura J. M. F, Hyperspectral imagery:clutter adaptation in anomaly detection. IEEE Trans. Information Theory 46(5),2000: 1855-1871
[112]Settle J. J., Drake N. A., Linear mixing and estimation of ground cover proportions, Int. J. Remote Sensing,14(6),1993:1159-1177.
[113]Smith D.B. and Johnson K.S., Single-step purification of polypeptides expressed in Escheric hia coli as fusion protein with glutathione-s-transferase, Gene 67 1988:31-40
[114]Smith M. O., Sabol D. E., Adams J. B., Spectral mixture analysis-new strategies for the analysis of multispectral
[115]Smith M. O., Johnson P. E., and Adams J. B., "Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis," J. Geophys. Res., vol.90, February 1985:797-804.
[116]Stein D. W. J., Beaven S. G., Hoff L. E., Winter E. M., Schaum A. P., Stocker A. D., Anomaly Detection from Hyperspectral Imagery, IEEE Signal Processing Magazine,19(1),2002:58-69.
[117]Thai B. and Healey G., Invariant subpixel target identification in hyperspectral imagery, Proc. SPIE,1999.
[118]Tompkins S., Mustard J. F., Pieters C. M., and Forsyth D. W., Optimization of endmembers for spectral mixture analysis, Remote sensing of environment, 59(3),1997:472-489.
[119]Tu T. M., Chen C. H., Chang C. I., A Posteriori Least Squares Orthogonal Subspace Projection Approach to Desired Signature Extraction and Detection, IEEE Transactions on Geoscience and Remote Sensing, Vol.35(1), 1997:127-139.
[120]Tu T. M., Chen C. H., Chang C. I., A Noise Subspace Projection Approach to Target Signature Detection and Extraction in an Unknown Background for hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, 36(1),1999:171-181.
[121]Wu K., Niu R., Zhang L., and Li P., A neural network method of selective endmember for pixel unmixing, in 27th Asian Conference on Remote Sensing (ACRS), Oct.,2006.
[122]Yu Xiaoli, Reed Irving S., Alan D. Stocker, Comparative performance Analysis of Adaptive Multispectral Detectors, IEEE Transactions on Signal Processing,41(8),1993:2639-2656.
[123]Zhang L., Du Bo, Zhong Y., Hybrid Detectors Based on Selective Endmembers, IEEE Transactions on Geoscience and Remote Sensing,2010, DOI: 10.1109/TGRS.2010.2040284.