合成孔径雷达图像目标识别技术研究
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摘要
基于合成孔径雷达(Synthetic Aperture Radar,SAR)的自动目标识别(Automatic Target Recognition,ATR)技术在现代战场中的重要作用已经使得它成为了国内外研究的热点之一。近十几年来,SAR图像的目标识别研究在SAR图像的预处理、特征提取及识别等方面均取得了相当大的进展。本论文主要围绕教育部留学回国人员基金“基于SAR图像的雷达自动目标检测与识别技术”、“十五”国防预研项目“目标识别技术”和“十一五”国防预研项目“基于目标成像的识别技术”的研究任务,针对合成孔径雷达图像的目标识别,从SAR图像滤波、SAR图像分割以及SAR图像的特征提取与识别等方面展开了较为深入的研究。本论文的主要内容概括如下:
1、针对SAR图像的相干斑特点,对比了几种常用的SAR图像滤波方法,分析了它们的优缺点,并给出了不同分辨率的SAR图像的滤波结果。
2、针对相关文献中存在的问题:①没有考虑阴影,而阴影信息对识别是很有用的;②没有将目标及其阴影从杂波背景中提取出来,由于背景杂波具有多样性,不同的背景杂波特性会影响识别性能;③对于一个特定的识别问题,采用某一种分类算法或者某一种特征未必能获得很好的识别性能,基于多特征或多分类算法的分类器融合是必需的,我们提出一种基于多分类器融合的SAR图像目标识别方法,首先给出有效的SAR图像预处理方法,将目标及其阴影从杂波背景中提取出来,抑制了背景杂波对后续识别的影响,然后基于极化映射提取目标的强度分布特征、目标和阴影的形状特征等,最后基于平均准则融合多个分类器。
3、主分量分析(Principal Component Analysis,PCA)是模式识别领域中一种经典的特征提取方法。然而,当PCA用于图像的特征提取时,要将2维图像矩阵(m×n)转换成1维向量(m ? n),这会带来两个方面的问题:①损失图像的2维空间结构信息;②特征提取要在高维向量空间中进行,但在高维空间中很难准确估计协方差矩阵,且维数很大(( m ? n )×( m ? n)),对其进行特征分解会大大增加计算负担。为了解决上述问题,二维主分量分析(Two-dimensional PCA,2DPCA)应运而出,它直接采用2维图像矩阵估计训练样本的协方差矩阵,估计得到的协方差矩阵更准确有效且维数仅为n×n或m×m,对其特征分解的效率也更高。但是,2DPCA仅去除了图像各行或各列像素间的冗余信息,因此得到的特征矩阵维数较大。本章首先根据投影形式的不同将2DPCA分为两种:右投影形式的2DPCA(Right 2DPCA,R-2DPCA)和左投影形式的2DPCA(Left 2DPCA,L-2DPCA)。为了降低特征维数、改善识别性能,给出相应的改进2DPCA方法。
4、线性判决分析(Linear Discriminant Analysis,LDA)也是模式识别领域中一种有效的特征提取方法。与PCA类似,LDA在用于图像特征提取时,也需要将2维图像矩阵转化为1维图像向量,这会带来“维数灾难”和“奇异”等问题。为此,近年来提出二维线性判决分析(Two-dimensional LDA,2DLDA),它直接基于2维图像矩阵来构建散布矩阵。本章中根据投影形式的不同首先将2DLDA分为两种形式:右投影的二维线性判决分析(Right 2DLDA,R-2DLDA)和左投影的二维线性判决分析(Left 2DLDA,L-2DLDA)。然后针对它们特征维数过大的缺陷,提出三种改进算法:两级R-2DLDA(Two-stage R-2DLDA)、两级L-2DLDA(Two-stage L-2DLDA)和两向2DLDA(Two-directional 2DLDA)。
5、LDA是一种被广泛应用的线性降维算法,但它要求各个类别的数据要满足单模分布结构,且在用于图像特征提取时通常会出现类内散布矩阵奇异的问题,还有LDA得到的特征维数仅为c? 1(c为类别数)。为了缓解上述局限性,近来提出了子类判别分析(Clustering-based Discriminant Analysis,CDA),它假设数据服从多模分布。然而,由于该方法采用的子类划分方法是k均值聚类算法,因而不能保证最终的识别结果是稳定的、最优的、且依赖聚类初始中心的选择。为此,我们给出一种改进的子类判决分析(Improved CDA,ICDA)方法,它首先采用快速全局k-均值聚类算法找到每类目标最优的子类划分,然后基于这些子类划分采用CDA准则找到最优的投影矢量,因此最终的识别性能不依赖聚类时初始聚类中心的选择,且能保证达到全局最优。
6、针对SAR图像数据的多模分布特性,提出了以下几种图像特征提取方法:(1)提出二维子类判决分析(Two-dimensional CDA,2DCDA)及其改进的算法。它们直接基于2维图像矩阵构造子类类间和子类类内散布矩阵,可克服CDA的维数灾难、奇异等问题。本章先后给出2DCDA的两种投影形式:右投影形式的2DCDA(Right 2DCDA,R-2DCDA)和左投影形式的2DCDA(Left 2DCDA,L-2DCDA)。针对它们求得的特征矩阵维数过大的问题,相应地提出几种改进的2DCDA算法。(2)提出二维最大子类散度差鉴别分析及其改进算法。由于在2DCDA中,需要计算子类类内散布矩阵的逆矩阵,而在小样本问题中逆矩阵通常是不存在的。为了避免计算逆矩阵或逆矩阵不存在的问题,我们给出一种新的图像特征提取方法:二维最大子类散度差鉴别分析(Two-dimensional Maximum Clustering-based Scatter Difference,2DMCSD)。由于2DMCSD只沿行方向压缩图像,类似地,给出其另一种形式(称之为Alternative 2DMCSD),它只沿列方向压缩图像。为了克服2DMCSD和Alternative 2DMCSD的特征维数过大的问题,又提出两向二维最大子类散度差鉴别分析(Two-directional 2DMCSD ,(2D)2MCSD)。(3)提出对角子类判决分析算法。上述的2DPCA、2DFLD和2DCDA仅保留了图像行(或列)方向的相关性变化,而忽略了图像列(或行)方向的相关性变化。为了同时保留图像行和列像素间的相关性变化,提出对角子类判决分析(Diagonal CDA,DiaCDA)。它基于对角图像寻找最优的投影方向,且考虑了每类数据中存在多个子类的情况。为了缓解DiaCDA的特征维数过大的问题,将DiaCDA和2DCDA结合起来,提出DiaCDA+2DCDA的特征提取方法。
Automatic target recognition (ATR) based on synthetic aperture radar (SAR) images is of great importance in the modern battlefield and has become a very hot research topic. In recent years, ATR based on SAR images has made great progress in related techniques including SAR images preprocessing, feature extraction, classifier design, and so on. This dissertation provides our researches for SAR target recognition, which are supported by Advanced Defense Research Programs of China and Natural Science Foundations of China.
The main content of this dissertation is summarized as follows:
1. In the first part, several SAR images filtering methods are first analyzed and compared according to the characteristics of the SAR speckles, and then the filtered results of some SAR images with different resolutions are presented.
2. To solve the problems in many literatures, a SAR ATR method based classifier fusion is proposed where target and shadow images are first segmented via SAR image pre-processing, and then the shape information of target and its shadow and the intensity distributed information of a target are extracted based on polar mapping, and shape descriptors of a target and its shadow are also extracted, finally SAR targets are classified by the combined classifier based on the average rule.
3. Principal component analysis (PCA) is a classical method in the pattern recognition area. However, when PCA is used to feature extraction for 2D images, 2D image matrices need to be transformed into 1D image vectors, this will bring on some problems such as a loss of 2D space structure information, disaster of dimensionality, etc. To solve these problems, 2DPCA is proposed recently where the projection directions are sought out from 2D image matrices, thus being more efficient. In this dissertation, 2DPCA is divided into right 2DPCA (R-2DPCA) and left 2DPCA (L-2DPCA) according to the ways of projection. To overcome the problem of more features of 2DPCA, we present four improved methods which not only can reduce feature dimensions but also can improve recognition performances.
4. Linear discriminant analysis (LDA) is also a popular feature extraction method in the pattern recognition field. Similar to PCA, when LDA is used to 2D images recognition tasks, some problems (such as a loss of 2D space structure information, disaster of dimensionality, singularity, etc) will occur. Therefore, 2DLDA is presented to solve the above problems, which constructs the scatter matrices based on 2D image matrices. 2DLDA can also be divided into right 2DLDA (R-2DLDA) and left 2DLDA (L-2DLDA). However, a drawback of R-2DLDA and L-2DLDA is that they need more features. To overcome this problem, we propose three improved approaches, two-stage R-2DLDA, two-stage L-2DLDA and two-directional 2DLDA.
5. LDA is a popular method for linear dimensionality reduction. LDA assumes that all the classes obey the unimodal distribution, singularity usually occurs when used to 2D image recognition tasks, and the dimensionality of features obtained by LDA is only c ? 1 ( c is the total number of classes). To alleviate the above-mentioned limitations, clustering-based discriminant analysis (CDA) is presented recently. In this method, k-means algorithm is employed for finding the clusters of each class, thus the final recognition results are unstable, not optimal and depend on the initial cluster centers. In this dissertation, we propose an improved CDA (ICDA) where the fast global k-means clustering algorithm is employed for finding the optimal cluster structures, thus the final results are stable and optimal.
6. This section presents several image feature extraction methods, which aim at dealing with the multimodal distributions of SAR images. The main work concerns the following three aspects: (1) Two-dimensional CDA (2DCDA) and its improved algorithms are developed. 2DCDA constructs the cluster scatter matrices from 2D image matrices, thus overcoming the problems (such as disaster of dimensionality, singularity, etc.) of CDA, and hence 2DCDA combines the capability to model the multiple cluster structures embedded within a single class with the computational advantage that is characteristic of 2D subspace analysis methods, such as 2DPCA and 2DLDA. In this section, 2DCDA is also divided into right 2DCDA (R-2DCDA) and left 2DCDA (L-2DCDA). Moreover, in order to solve the problem of too much features of 2DCDA, we propose four improved algorithms, two-stage R-2DCDA, two-stage L-2DCDA, two-directional 2DCDA ((2D)2CDA), and generalized 2DCDA (G2DCDA). (2) Two-dimensional maximum clustering-based scatter difference discriminant analysis (2DMCSD) and its improved algorithms are proposed. In 2DCDA, the inverse matrix of the within-cluster scatter matrix has to be calculated, however, usually the inverse matrix does not exist in“small sample size”(SSS) problems. To solve this problem, we propose a novel image feature extraction method, 2DMCSD, which adopts the difference of between-cluster scatter and within-cluster scatter as the discriminant criterion for finding the projection vectors. So it not only can deal with the multimodal distribution problems but also is capable of avoiding the inverse matrix calculation and the“SSS”problems. Besides, an alternative 2DMCSD is presented. Two-directional 2DMCSD ((2D)2MCSD) discriminant analysis is also developed for further dimensionality reduction. (3) Diagonal CDA (DiaCDA) is proposed. In 2DPCA, 2DLDA and 2DCDA, the projection vectors only reflect the variations between rows (or columns) of images while the omitted variations between columns (or rows) of images may be also useful for recognition. To preserve the correlations between variations of both rows and columns of images, diagonal CDA (DiaCDA) is proposed, which seeks the optimal projection vectors from the diagonal images and takes into account the possibility of embedded multiple cluster structures within a single class. However, an inextricable problem of DiaCDA is that it requires vast memory for representation of images. To alleviate this problem, we combine DiaCDA with 2DCDA (DiaCDA+2DCDA).
1、针对SAR图像的相干斑特点,对比了几种常用的SAR图像滤波方法,分析了它们的优缺点,并给出了不同分辨率的SAR图像的滤波结果。
2、针对相关文献中存在的问题:①没有考虑阴影,而阴影信息对识别是很有用的;②没有将目标及其阴影从杂波背景中提取出来,由于背景杂波具有多样性,不同的背景杂波特性会影响识别性能;③对于一个特定的识别问题,采用某一种分类算法或者某一种特征未必能获得很好的识别性能,基于多特征或多分类算法的分类器融合是必需的,我们提出一种基于多分类器融合的SAR图像目标识别方法,首先给出有效的SAR图像预处理方法,将目标及其阴影从杂波背景中提取出来,抑制了背景杂波对后续识别的影响,然后基于极化映射提取目标的强度分布特征、目标和阴影的形状特征等,最后基于平均准则融合多个分类器。
3、主分量分析(Principal Component Analysis,PCA)是模式识别领域中一种经典的特征提取方法。然而,当PCA用于图像的特征提取时,要将2维图像矩阵(m×n)转换成1维向量(m ? n),这会带来两个方面的问题:①损失图像的2维空间结构信息;②特征提取要在高维向量空间中进行,但在高维空间中很难准确估计协方差矩阵,且维数很大(( m ? n )×( m ? n)),对其进行特征分解会大大增加计算负担。为了解决上述问题,二维主分量分析(Two-dimensional PCA,2DPCA)应运而出,它直接采用2维图像矩阵估计训练样本的协方差矩阵,估计得到的协方差矩阵更准确有效且维数仅为n×n或m×m,对其特征分解的效率也更高。但是,2DPCA仅去除了图像各行或各列像素间的冗余信息,因此得到的特征矩阵维数较大。本章首先根据投影形式的不同将2DPCA分为两种:右投影形式的2DPCA(Right 2DPCA,R-2DPCA)和左投影形式的2DPCA(Left 2DPCA,L-2DPCA)。为了降低特征维数、改善识别性能,给出相应的改进2DPCA方法。
4、线性判决分析(Linear Discriminant Analysis,LDA)也是模式识别领域中一种有效的特征提取方法。与PCA类似,LDA在用于图像特征提取时,也需要将2维图像矩阵转化为1维图像向量,这会带来“维数灾难”和“奇异”等问题。为此,近年来提出二维线性判决分析(Two-dimensional LDA,2DLDA),它直接基于2维图像矩阵来构建散布矩阵。本章中根据投影形式的不同首先将2DLDA分为两种形式:右投影的二维线性判决分析(Right 2DLDA,R-2DLDA)和左投影的二维线性判决分析(Left 2DLDA,L-2DLDA)。然后针对它们特征维数过大的缺陷,提出三种改进算法:两级R-2DLDA(Two-stage R-2DLDA)、两级L-2DLDA(Two-stage L-2DLDA)和两向2DLDA(Two-directional 2DLDA)。
5、LDA是一种被广泛应用的线性降维算法,但它要求各个类别的数据要满足单模分布结构,且在用于图像特征提取时通常会出现类内散布矩阵奇异的问题,还有LDA得到的特征维数仅为c? 1(c为类别数)。为了缓解上述局限性,近来提出了子类判别分析(Clustering-based Discriminant Analysis,CDA),它假设数据服从多模分布。然而,由于该方法采用的子类划分方法是k均值聚类算法,因而不能保证最终的识别结果是稳定的、最优的、且依赖聚类初始中心的选择。为此,我们给出一种改进的子类判决分析(Improved CDA,ICDA)方法,它首先采用快速全局k-均值聚类算法找到每类目标最优的子类划分,然后基于这些子类划分采用CDA准则找到最优的投影矢量,因此最终的识别性能不依赖聚类时初始聚类中心的选择,且能保证达到全局最优。
6、针对SAR图像数据的多模分布特性,提出了以下几种图像特征提取方法:(1)提出二维子类判决分析(Two-dimensional CDA,2DCDA)及其改进的算法。它们直接基于2维图像矩阵构造子类类间和子类类内散布矩阵,可克服CDA的维数灾难、奇异等问题。本章先后给出2DCDA的两种投影形式:右投影形式的2DCDA(Right 2DCDA,R-2DCDA)和左投影形式的2DCDA(Left 2DCDA,L-2DCDA)。针对它们求得的特征矩阵维数过大的问题,相应地提出几种改进的2DCDA算法。(2)提出二维最大子类散度差鉴别分析及其改进算法。由于在2DCDA中,需要计算子类类内散布矩阵的逆矩阵,而在小样本问题中逆矩阵通常是不存在的。为了避免计算逆矩阵或逆矩阵不存在的问题,我们给出一种新的图像特征提取方法:二维最大子类散度差鉴别分析(Two-dimensional Maximum Clustering-based Scatter Difference,2DMCSD)。由于2DMCSD只沿行方向压缩图像,类似地,给出其另一种形式(称之为Alternative 2DMCSD),它只沿列方向压缩图像。为了克服2DMCSD和Alternative 2DMCSD的特征维数过大的问题,又提出两向二维最大子类散度差鉴别分析(Two-directional 2DMCSD ,(2D)2MCSD)。(3)提出对角子类判决分析算法。上述的2DPCA、2DFLD和2DCDA仅保留了图像行(或列)方向的相关性变化,而忽略了图像列(或行)方向的相关性变化。为了同时保留图像行和列像素间的相关性变化,提出对角子类判决分析(Diagonal CDA,DiaCDA)。它基于对角图像寻找最优的投影方向,且考虑了每类数据中存在多个子类的情况。为了缓解DiaCDA的特征维数过大的问题,将DiaCDA和2DCDA结合起来,提出DiaCDA+2DCDA的特征提取方法。
Automatic target recognition (ATR) based on synthetic aperture radar (SAR) images is of great importance in the modern battlefield and has become a very hot research topic. In recent years, ATR based on SAR images has made great progress in related techniques including SAR images preprocessing, feature extraction, classifier design, and so on. This dissertation provides our researches for SAR target recognition, which are supported by Advanced Defense Research Programs of China and Natural Science Foundations of China.
The main content of this dissertation is summarized as follows:
1. In the first part, several SAR images filtering methods are first analyzed and compared according to the characteristics of the SAR speckles, and then the filtered results of some SAR images with different resolutions are presented.
2. To solve the problems in many literatures, a SAR ATR method based classifier fusion is proposed where target and shadow images are first segmented via SAR image pre-processing, and then the shape information of target and its shadow and the intensity distributed information of a target are extracted based on polar mapping, and shape descriptors of a target and its shadow are also extracted, finally SAR targets are classified by the combined classifier based on the average rule.
3. Principal component analysis (PCA) is a classical method in the pattern recognition area. However, when PCA is used to feature extraction for 2D images, 2D image matrices need to be transformed into 1D image vectors, this will bring on some problems such as a loss of 2D space structure information, disaster of dimensionality, etc. To solve these problems, 2DPCA is proposed recently where the projection directions are sought out from 2D image matrices, thus being more efficient. In this dissertation, 2DPCA is divided into right 2DPCA (R-2DPCA) and left 2DPCA (L-2DPCA) according to the ways of projection. To overcome the problem of more features of 2DPCA, we present four improved methods which not only can reduce feature dimensions but also can improve recognition performances.
4. Linear discriminant analysis (LDA) is also a popular feature extraction method in the pattern recognition field. Similar to PCA, when LDA is used to 2D images recognition tasks, some problems (such as a loss of 2D space structure information, disaster of dimensionality, singularity, etc) will occur. Therefore, 2DLDA is presented to solve the above problems, which constructs the scatter matrices based on 2D image matrices. 2DLDA can also be divided into right 2DLDA (R-2DLDA) and left 2DLDA (L-2DLDA). However, a drawback of R-2DLDA and L-2DLDA is that they need more features. To overcome this problem, we propose three improved approaches, two-stage R-2DLDA, two-stage L-2DLDA and two-directional 2DLDA.
5. LDA is a popular method for linear dimensionality reduction. LDA assumes that all the classes obey the unimodal distribution, singularity usually occurs when used to 2D image recognition tasks, and the dimensionality of features obtained by LDA is only c ? 1 ( c is the total number of classes). To alleviate the above-mentioned limitations, clustering-based discriminant analysis (CDA) is presented recently. In this method, k-means algorithm is employed for finding the clusters of each class, thus the final recognition results are unstable, not optimal and depend on the initial cluster centers. In this dissertation, we propose an improved CDA (ICDA) where the fast global k-means clustering algorithm is employed for finding the optimal cluster structures, thus the final results are stable and optimal.
6. This section presents several image feature extraction methods, which aim at dealing with the multimodal distributions of SAR images. The main work concerns the following three aspects: (1) Two-dimensional CDA (2DCDA) and its improved algorithms are developed. 2DCDA constructs the cluster scatter matrices from 2D image matrices, thus overcoming the problems (such as disaster of dimensionality, singularity, etc.) of CDA, and hence 2DCDA combines the capability to model the multiple cluster structures embedded within a single class with the computational advantage that is characteristic of 2D subspace analysis methods, such as 2DPCA and 2DLDA. In this section, 2DCDA is also divided into right 2DCDA (R-2DCDA) and left 2DCDA (L-2DCDA). Moreover, in order to solve the problem of too much features of 2DCDA, we propose four improved algorithms, two-stage R-2DCDA, two-stage L-2DCDA, two-directional 2DCDA ((2D)2CDA), and generalized 2DCDA (G2DCDA). (2) Two-dimensional maximum clustering-based scatter difference discriminant analysis (2DMCSD) and its improved algorithms are proposed. In 2DCDA, the inverse matrix of the within-cluster scatter matrix has to be calculated, however, usually the inverse matrix does not exist in“small sample size”(SSS) problems. To solve this problem, we propose a novel image feature extraction method, 2DMCSD, which adopts the difference of between-cluster scatter and within-cluster scatter as the discriminant criterion for finding the projection vectors. So it not only can deal with the multimodal distribution problems but also is capable of avoiding the inverse matrix calculation and the“SSS”problems. Besides, an alternative 2DMCSD is presented. Two-directional 2DMCSD ((2D)2MCSD) discriminant analysis is also developed for further dimensionality reduction. (3) Diagonal CDA (DiaCDA) is proposed. In 2DPCA, 2DLDA and 2DCDA, the projection vectors only reflect the variations between rows (or columns) of images while the omitted variations between columns (or rows) of images may be also useful for recognition. To preserve the correlations between variations of both rows and columns of images, diagonal CDA (DiaCDA) is proposed, which seeks the optimal projection vectors from the diagonal images and takes into account the possibility of embedded multiple cluster structures within a single class. However, an inextricable problem of DiaCDA is that it requires vast memory for representation of images. To alleviate this problem, we combine DiaCDA with 2DCDA (DiaCDA+2DCDA).
引文
[1]吴顺君,梅晓春.雷达信号处理和数据处理技术.北京:电子工业出版社, 2008.
[2]黄培康,殷红成,许小剑.雷达目标特性分析.北京:电子工业出版社, 2005.
[3]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社, 2005.
[4]张澄波.综合孔径雷达.北京:科学出版社, 1989.
[5]邢孟道.基于实测数据的雷达成像方法研究.博士研究生学位论文. 2002.
[6] C. A. Wiley. Synthetic aperture radar. IEEE Trans. on Aerospace and Electronic Systems, Vol. 21(3), 440-443, 1985.
[7] W. G. Carrara, R. S. Goodman, and R. M. Majewski. Spotlight synthetic aperture radar: signal processing algorithms. Boston: Artech House, 1995.
[8]马长征.雷达目标三维成像技术研究.博士研究生学位论文.西安电子科技大学. 1999.
[9]姜正林.低分辨ISAR成像及干涉技术应用研究.博士研究生学位论文.西安电子科技大学. 2001.
[10]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[11]杜兰,刘宏伟,保铮.一种基于距离-多谱勒二维联合的群目标分辨方法.电子学报, Vol. 32(6), 881-885, 2004.
[12]杜兰,保铮,邢孟道.直升机雷达回波的分析与检测.西安电子科技大学学报, Vol. 30(5), 574-579, 2003.
[13]廖学军.基于高分辨距离像的雷达目标识别.博士研究生学位论文.西安电子科技大学. 1999。
[14]裴炳南.高分辨雷达自动目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2002.
[15]刘宏伟,杜兰,袁莉,保铮.雷达高分辨距离像目标识别研究进展.电子与信息学报, Vol. 27(8), 1328-1334, 2005.
[16]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[17]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[18]陈渤.基于核方法的雷达高分辨距离像目标识别技术研究.博士研究生学位论文.西安电子科技大学. 2008.
[19] Hongwei Liu, Zheng Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel. Lecture Notes in Computer Science, Vol. 3174 (I), 531-536, 2004.
[20] Lan Du, Hongwei Liu, Zheng Bao and Mengdao Xing. Radar HRRP target recognition based on higher order spectra. IEEE Trans. on Signal Processing, Vol. 53(7), 2359-2368, 2005.
[21]余志舜,朱兆达.逆合成孔径雷达横向距离定标.电子学报, Vol. 2(6), 45-48, 1997.
[22]李强.单脉冲雷达目标三维成像与识别研究.博士研究生学位论文.西安电子科技大学. 2007.
[23] Qun Zhang and Tat Soon Yeo. Three-dimensional SAR imaging of a groundmoving target using the InISAR technique. IEEE Trans. on Geoscience and Remote Sensing, Vol. 42(9), 1818-1828, 2004.
[24]马长征,张守宏.舰船目标单脉冲雷达三维成像技术.电子科学学刊, 22(3), 385-391, 2000.
[25] Kim Kyung-Tae, Seo Dong-Kyu, and Kim Hyo-Tae. Efficient classification of ISAR images. IEEE Trans. on antennas and propagation, Vol. 53(5), 1611-1621, 2005.
[26] S. Musman, D. Kerr, and C. Bachmann. Automatic recognition of ISAR ship images. IEEE Trans. on Aerospace and Electronics Systems, Vol. 32(4), 1392-1404, 1996.
[27]黄玉琴.基于SAR图像的城市形态时空变化的研究.中国科学院研究生院(遥感应用研究所).博士研究生学位论文. 2006.
[28]付琨.高分辨率单视单极化SAR图像地物分类方法研究.国防科学技术大学.博士研究生学位论文. 2002.
[29]胡丹.基于SVM的SAR图像地物分类研究.中南大学.硕士研究生学位论文. 2008.
[30]傅斌. SAR浅海水下地形探测.中国海洋大学.博士研究生学位论文. 2005.
[31]谭衢霖.鄱阳湖湿地生态环境遥感变化监测研究.中国科学院研究生院(遥感应用研究所).博士研究生学位论文. 2002.
[32]薛浩洁. SAR图像海洋表面油膜检测方法研究.中国科学院研究生院(电子学研究所).硕士研究生学位论文. 2004.
[33]贾承丽. SAR图像道路和机场提取方法研究.国防科学技术大学.博士研究生学位论文. 2006.
[34]蒋斌. SAR图像道路提取方法研究.国防科学技术大学.硕士研究生学位论. 2004.
[35]蒋运辉. SAR图像中的道路检测技术研究.电子科技大学.硕士研究生学位论文. 2005.
[36]戴光照,张荣.高分辨率SAR图像中的桥梁识别方法研究.遥感学报, Vol. 11(2), 177-184, 2007.
[37]曹海梅.遥感图像中水上桥梁目标识别与毁伤分析研究.南京理工大学.硕士研究生学位论文. 2005.
[38]邹英.水上桥梁目标识别算法研究.南京理工大学.硕士研究生学位论文. 2006.
[39]琚春华,王伟明,王光明,姚庆栋.一种桥梁目标识别方法的研究.计算机工程, Vol. 25(6), 19-20, 1999.
[40]李海艳.极化SAR图像海面船只监测方法研究.中国科学院研究生院(海洋研究所).博士研究生学位论文. 2007.
[41]匡纲要,计科峰,粟毅,郁文贤. SAR图像自动目标识别研究.中国图象图形学报, Vol. 8(A), 1115-1120, 2003.
[42]袁礼海,宋建社,薛文通,赵伟舟. SAR图像自动目标识别系统研究与设计.计算机应用研究, Vol. 11, 249-251, 2006.
[43] T. Ross, S. Worrell, V. Velten, J. Mossing, and M. Bryant. Standard SAR ATR evaluation experiments using the MSTAR public release data set. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[44] M. Bryant, S. Worrell, and A. Dixon. MSE template size analysis for MSTAR data. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 396-405, 1998.
[45] L. M. Kaplan. Analysis of multiplicative speckle models for template-based SAR ATR. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(4), 1424-1432, 2001.
[46] L. M.Kaplan, R. Murenzi, E. Asika, and K. Namuduri. Effect of signal-to-clutter ratio on template-based ATR. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 408-419, 1998.
[47] R. Sharma and N. S. Subotic. Construction of hybrid templates from collected and simulated data for SAR ATR algorithms. Proceedings of SPIE -The International Society for Optical Engineering, Vol. 3371, 480-486, 1998.
[48] M. Bryant, S. Worrell, and S. Parker. Class separability assessments and MSE algorithm robustness. Proceedings of SPIE -The International Society for Optical Engineering, Vol. 3070, 294-305, 1997.
[49]罗峰.合成孔径雷达图像自动目标识别方法研究.四川大学.硕士研究生学位论文.
[50]韩萍,吴仁彪,蒋立辉.一种有效的SAR图像分割与目标识别方法.系统工程与电子技术, Vol. 26(6), 737-737, 2004.
[51] A. S. Stephen, Eric Keydel, et al. The use of the mean squared error matching metric in a model-based automatic target recognition system. SPIE, 1998, Vol.3370, 360-368.
[52] B. Bhanu. Automatic target recognition: state of the art survey. IEEE Trans. on Aerospace and Electronic Systems, 1986, Vol. 22(4), 364-379.
[53] B. Bhanu, D. E. Dudgeon, et al. Introduction to the special issue on automatic target detection and recognition. IEEE Trans. on Image Processing, 1997, Vol. 6(1), 1-614.
[54] B. Bhanu, T. Jones. Image understanding research for automatic target recognition. IEEE Aerospace and Electronics Systems Magazine, 1993, Vol. 8(10), 15-23.
[55] J. A. Ratches, Waltes, et al. Aided and automatic target recognition based upon sensory inputs from images forming systems. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, Vol. 19(9), 1004-1019.
[56] G. J. Ettinger, G. A. Klanderman, et al. Probabilistic optimization approach to SAR feature matching. SPIE, 1996, Vol. 2757, 318-329.
[57] J. Wissinger, R. Washburn, and D. Morgan, et al. Search algorithms for model-based SAR ATR. SPIE, 1996, Vol. 2757, 279-293.
[58] R. K. Eric and S. W. Lee. Signature prediction for model-based automatic target recognition. SPIE, 1996, Vol.2757, 306-317.
[59] Q. Zhao and J. C. Principe. Support vector machines for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol.37(2), 643-654, 2001.
[60] M. Bryant and F. Garber. SVM classifier applied to the MSTAR public data set. SPIE, 1999, Vol.3721, 355-360.
[61]詹新光.基于支持向量机的SAR图像目标识别.西安科技大学.硕士研究生学位论文. 2008.
[62]宦若虹,杨汝良,岳晋. SVM与HMM相结合的合成孔径雷达图像目标识别.系统工程与电子技术, Vol. 30(3), 447-451, 2008.
[63]宦若虹,杨汝良.基于ICA和SVM的SAR图像特征提取与目标识别.计算机工程, Vol. 34(13), 24-28, 2008.
[64] M. Alberto. Improved multilook techniques applied to SAR and SCANSAR images. IEEE Trans. on Geoscience and Remote Sensing, Vol. 23(4), 529-534, 1991.
[65] J. S. Lee. A simple smoothing algorithm for synthetic aperture radar images. IEEE Trans. on System, Man, and Cybernetics, Vol. 13(1), 95-89, 1983.
[66] J. S. Lee. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, Vol. 25(5), 636-643, 1986.
[67] D. T. Kuan and A. A. Sawchuk. Adaptive noise smoothing filter for signal-dependent noise. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 7(2), 165-177, 1985.
[68] A. Lopes, E. Nezry and R. Touzi. Maximum a posteriori speckle and first order texture model in SAR images. Proceeding of the IGASS90. Maryland: University of Maryland, 2409-2412, 1990.
[69]付琨,匡纲要,郁文贤.基于改进相关邻域模型的SAR图像RCS重构.系统工程与电子技术, Vol.23(4), 48-53, 2001.
[70]鹂苏丹,张翠,王正志,李广侠.一种基于相关邻域模型的SAR图像迭代滤波方法.数据采集与处理, Vol.18(2), 179-184, 2003.
[71]邓炜,赵荣椿.基于小波变换的SAR图像相干斑噪声消除方法研究.信号处理, Vol. 17(1), 86-90, 2001.
[72] J. P. Greory and A. W. Robert. ATR subsystem performance measure using manual segmentation of SAR target chips. SPIE, Vol. 3721, 685-692, 1999.
[73]何壸. SAR图像分割及目标识别.西安电子科技大学.硕士研究生学位论文. 2006.
[74]彭丽娟. SAR图像去噪及分割.西安科技大学.硕士研究生学位论文. 2006.
[75]付琨,匡纲要,郁文贤.一种合成孔径雷达图像阴影和目标检测的方法.软件学报, Vol. 13(4), 818-826, 2002.
[76]李响,韩萍,吴仁彪,杨国庆.一种基于Weibull分布的SAR图像分割方法.系统工程与电子技术, Vol. 29(5), 677-679, 2007.
[77]韩萍,吴仁彪,蒋立辉.一种有效的SAR图像分割与目标识别方法.系统工程与电子技术, Vol. 26(6), 734-737, 2004.
[78]成功,毛士艺. MSTAR图像中目标和阴影的局部特征提取及分类研究.全国第十届信号与信息处理、第四届DSP应用技术联合学术会议, 2006.
[79]王义敏,安锦文,王若朴.基于局部双参数估计的SAR图像CFAR目标检测技术.火力与指挥控制, Vol. 31(4), 50-52, 2006.
[80]成功,赵巍,毛士艺.双阈值CFAR SAR目标分割方法.信号处理, Vol. 23(1), 9-14, 2007.
[81]张红蕾,宋建社,翟晓颖.一种基于二维最大熵的SAR图像自适应阈值分割算法.电光控制, Vol. 14(4), 63-65, 2007.
[82]郑宗贵,毛士艺. MSTAR图像分割算法研究.系统工程与电子技术, Vol. 24(12), 93-96, 2003.
[83]高贵,计科峰,匡纲要.基于马尔可夫随机场的SAR目标切片图像分割方法.现代雷达, Vol. 26(5), 42-45, 2004.
[84]鹂苏丹,张翠,王正志.基于马尔可夫随机场的SAR图象目标分割.中国图象图形学报, Vol. 7(A)(8), 794-799, 2002.
[85]曹兰英,夏良正,张昆辉.基于小波域MRF的SAR图像分割.东南大学学报, Vol. 34(6), 847-850, 2004.
[86]侯一民,郭雷.一种基于马尔可夫随机场的SAR图像分割新方法.电子与信息学报, Vol. 29(5), 1069-1072, 2007.
[87]张翠,郦苏丹,王正志. SAR图像目标分割与方位角估计.国防科技大学学报, Vol. 23(5), 74-78, 2001.
[88]郦苏丹,张翠,王正志. SAR图像的最优分割方法.遥感技术与应用, Vol. 16 (3), 195-199, 2001.
[89] R. A. Weisenssel, W. C. Karl, D. A. Castanon, G. J. Power and P. Douville. Markov random field segmentation methods for SAR target chips. SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, Vol. 3721, 462-473, 2000.
[90] A. Robert, W. Weisenseel, K. Clem, et al. MRF-based algorithms for segmentationof SAR images. The Proceeding of the 1998 International Conference on Image Processing. Paris: IEEE, 1998, 770-774.
[91] R. A. Weisenseel, W. C. Karl, D. A. Castanon, et al. Markov random field segmentation methods for SAR target chips. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, 1999, Vol. 3721, 462-473.
[92] E. Aitnour, S. Wang, and D. Ziou. Segmentation of small vehicle targets in SAR images. Proceedings of SPIE on Automatic Target Recognition XII, Vol. 4726, 35-45, 2002.
[93] R. Meth. Target/shadow segmentation and aspect estimation in synthetic aperture radar imagery. Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 188-196, 1998.
[94] K. S. Wilson and G. J. Power. Region growing shadow segmentation in synthetic aperture radar images. Proc of International Conference on Imaging Science, Systems, and Technology (CISST 2000), 2000.
[95] G. J. Powel and K. S. Wilson. Segmenting shadows from synthetic aperture radar imagery using edge-enhanced region growing. Proceedings of SPIE on Algorithms and Systems for Optical Information Processing IV, 2000, Vol. 4113, 106-114.
[96]王岩,梁甸农,郭汉伟.基于正则化增强的SAR图像分割方法.信号处理, Vol. 19(3), 227-229, 2003.
[97]张静,王国宏,刘福太.基于正则化方法的SAR图像分割及目标边缘检测算法.吉林大学学报, Vol. 38(1), 206-210, 2008.
[98]宦若虹,杨汝良,岳晋.水平集分割方法在合成孔径雷达图像目标识别中的应用.遥感技术与应用. Vol. 22(6), 681-684, 2007.
[99]田小林,焦李成,缑水平.基于PSO优化空间约束聚类的SAR图像分割.电子学报, Vol. 36(3), 453-457, 2008.
[100] G. J. Power. Inter-algorithmic evaluation of SAR image segmentation algorithms using multiple measures. Proceedings of SPIE on Algorithms for Synthetic Aperture Radar Imagery VIII, 2001, Vol. 4382, 330– 337.
[101] G. J. Power and A. A. S. Awwal. Optoelectronic complex inner product for evaluating quality of image segmentation. Proceedings of SPIE on PhotonicDevices and Algorithms for Computing II, 2000, Vol. 4114, 13– 19.
[102] Venkatachalam, Vidya, Hyeokho Choi, and Richard G. Baraniuk. Multiscale SAR image segmentation using wavelet-domain hidden markov tree models. Proceedings of SPIE -The International Society for Optical Engineering, 2000, Vol. 4053, 110-120.
[103] Venkatachalam, Vidya, Robert D. Nowak, Richard G. Baraniuk, and Mario A. T. Figueiredo. Unsupervised SAR image segmentation using recursive partitioning. Proceedings of SPIE -The International Society for Optical Engineering, Vol. 4053, 121-129, 2000.
[104] S. Beucher and F. Meyer. The morphological approach of segmentation: the watershed transformation. Mathematical Morphology in Image Processing. 1992.
[105]胡利平,刘宏伟,吴顺君.基于多分类器融合的SAR图像自动目标识别方法,系统工程与电子技术, Vol. 30(5), 839-842, 2008.
[106]胡利平,刘宏伟,吴顺君.一种新的SAR图像目标识别预处理方法.西安电子科技大学学报, Vol. 34(5), 733-737, 2007.
[107] M. P. Hurst and R. Mittra. Scattering center analysis via prony’s method. IEEE Trans. on Antennas and Propagation, Vol. 35, 986-989, 1987.
[108] L. C. Gerry and R. L. Moses. Attributed scattering centers for SAR ATR. IEEE Trans. on Image Processing, Vol. 6(1), 79-91, 1997.
[109] M. A. Koets. Feature extraction using attributed scattering models on SAR imagery. Proc of SPIE on Algorithms for Synthetic Aperture Radar Imagery VI, 1999, Vol. 3721, 104-115.
[110]计科峰,匡纲要,粟毅,郁文贤.一种改进的图像域SAR目标散射中心特征提取方法.电路与系统学报, Vol. 9(2), 40-44, 2004.
[111] T. D. Ross, J. J. Bradley, L. J. Hudson, and M. P. O'Connor. SAR ATR - so what's the problem?– an MSTAR perspective. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, 1999, Vol.3721, 662– 672.
[112]计科峰,匡纲要,郁文贤. SAR图像目标峰值提取与稳定性分析.现代雷达, Vol. 25(2), 15-18, 2003.
[113]高贵,计科峰,匡纲要,李德仁.高分辨SAR图像目标峰值提取及峰值稳定性分析.电子与信息学报, Vol. 27(4), 561-565, 2005.
[114]徐牧,王雪松,肖顺平.基于目标轮廓特征的SAR图像目标识别.系统工程及电子技术, Vol. 28(12), 1812-1815, 2006.
[115]王世晞,贺志国.基于PCA特征的快速SAR图像目标识别方法.国防科技大学学报, Vol. 30(3), 136-140, 2007.
[116]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR图像目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[117]韩萍,吴仁彪,王兆华.基于KFD准则的SAR目标特征提取与识别.现代雷达, Vol. 26(7), 27-30, 2004.
[118]成功,赵巍,毛士艺.基于快速提升KLDA准则的MSTAR SAR目标特征提取与识别研究.航空学报, Vol. 28(3), 667-672, 2007.
[119]徐牧,王雪松,肖顺平.基于Hough变换与目标主轴提取的SAR图像目标方位角估计方法.电子与信息学报, Vol. 29(2), 370-374, 2007.
[120]高贵,计科峰,匡纲要,李德仁.基于Radon变换的SAR图像目标统计不变特征提取研究.遥感技术与应用, Vol. 19(3), 187-192, 2004.
[121]胡利平,刘宏伟,吴顺君.基于两级2DPCA的SAR目标特征提取与识别.电子与信息学报, Vol. 30(7), 1722-1726, 2008.
[122]胡利平,刘宏伟,李丽亚,吴顺君.两向二维最大子类散度差鉴别分析及其在SAR目标识别中的应用.航空学报,已录用.
[123]胡利平,刘宏伟,尹奎英,吴顺君.基于G2DPCA的SAR目标特征提取与识别.宇航学报,已录用.
[124] Liping Hu, Jin Liu, Hongwei Liu, Bo Chen, Shunjun Wu. Automatic target recognition based on SAR images and two-stage 2DPCA features. APSAR2007. 1st Asian and Pacific Conference on Synthetic Aperture Radar, Huangshan, 801-805, 2007.
[125] Liping Hu, Hongwei Liu, Kuiying Yin, Shunjun Wu. Two-dimensional clustering-based discriminant analysis for synthetic aperture radar automatic target recognition. ISAPE2008. 8th International Symposium on Antenna, Propagation, and EM Theory, Kunming, 701-705, 2008.
[126] Liping Hu, Hongwei Liu, Shunjun Wu. Two-dimensional maximumclustering-based scatter difference discriminant analysis for synthetic aperture radar automatic target recognition. ISNN2009. 6th International Symposium on Neural Networks, Wuhan, 2009,已录用.
[127] Liping Hu, Hongwei Liu, Kuiying Yin, Shunjun Wu. Diagonal clustering-based discriminant analysis for synthetic aperture radar automatic target recognition. IET International Radar Conference, Guilin, 2009,已录用.
[128] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. Vol. 2, 121-167, 1998.
[129] R. R. Lawrence. A tutorial on hidden markov models and selected applications in speech recognition. Proceeding of the IEEE, Vol. 77(2), 257-286, 1989.
[130] E. R. Keydel, S. W. Lee, and J. T. Moore. MSTAR extended operating conditions: a tutorial. Proc of SPIE on algorithm for synthetic aperture radar imagery IV, Vol. 2757, 228-242, 1996.
[131] J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[132] S. C. Chen, Y. L. Zhu, D. Q. Zhang, and J. Y. Yang. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, Vol. 26, 1157-1167, 2005.
[133] D. Q. Zhang and Z. H. Zhou. (2D)2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing, Vol. 69, 224-231, 2005.
[134] M. Li and B. Yuan. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, 527-532, 2005.
[135] H. L. Xiong, M. N. S. Swamy, and M. O. Ahmad. Two-dimensional FLD for face recognition. Pattern Recognition, Vol. 38, 1121-1124, 2005.
[136] K. Fukunaga. Introduction to statistical pattern recognition. Boston, Academic Press Inc., 1990.
[137] X. W. Chen and T Huang. Facial expression recognition: a clustering-based approach. Pattern Recognition Letters, Vol. 24, 1295-1302, 2003.
[138] M. Zhu and A. M. Martinez. Subclass discriminant analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28(8), 1274-1286, 2006.
[139] C. Xiang and D. Huang. Feature extraction using recursive cluster-based linear discriminant with application to face recognition. IEEE Trans. image processing, Vol. 15(12), 3824-3832, 2006.
[140] A. Likas, N. Vlassis and J. J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, Vol. 36, 451-461, 2003.
[141]宋枫溪,程科,杨靖宇等.最大散度差和大间隔线性投影与支持向量机.自动化学报, Vol. 30(6), 890-896, 2004.
[142]刘永俊,陈才扣.最大散度差鉴别分析及人脸识别.计算机工程与应用, Vol. 34, 208-210, 2006.
[143] Song F X, Zhang D, Mei D Y, et al. A multiple maximum scatter difference discriminant criterion for facial feature extraction. IEEE Trans. on System, Man, and Cybernetic-Part B: Cybernetics, Vol. 37(6), 1599-1606, 2007.
[144] D. Q. Zhang, Z. H. Zhou, and S. C. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, Vol. 39 (1), 140-142, 2006.
[145] S. Noushath, G. H. Kumar, P. Shivakumara. Diagonal fisher linear discriminant analysis for efficient face recognition. Neurocomputing, Vol. 69, 1711-1716, 2006.
[1] M. Alberto. Improved multilook techniques applied to SAR and SCANSAR images. IEEE Trans. on Geoscience and Remote Sensing, Vol. 23(4), 529-534, 1991.
[2]邓炜,赵荣椿.基于小波变换的SAR图像相干斑噪声消除方法研究.信号处理, Vol. 17(1), 86-90, 2001.
[3] J. S. Lee. A simple smoothing algorithm for synthetic aperture radar images. IEEE Trans. on System, Man, and Cybernetics, Vol. 13(1), 95-89, 1983.
[4] J. S. Lee. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, Vol. 25(5), 636-643, 1986.
[5] D. T. Kuan and A. A. Sawchuk. Adaptive noise smoothing filter for signal-dependent noise. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 7(2), 165-177, 1985.
[6] A. Lopes, E. Nezry and R. Touzi. Maximum a posteriori speckle and first order texture model in SAR images. Proceeding of the IGASS90. Maryland: University of Maryland, 2409-2412, 1990.
[7] C. Oliver and S. Quegan. Understanding synthetic aperture radar images. New York: Artech House, 1998.
[8] Henri Maitre编,孙洪等译.合成孔径雷达图像处理.北京:电子工业出版社,2005.
[9]付琨,匡纲要,郁文贤.基于改进相关邻域模型的SAR图像RCS重构.系统工程与电子技术, Vol.23(4), 48-53, 2001.
[10]盛国芳,冷朝霞,焦李成.数据融合在SAR图像去噪中的应用.西安电子科技大学学报, Vol. 31(1), 91-95, 2004.
[11]C. J. Oliver. A model for non-rayleigh scattering statistics. Optical Acta., Vol. 31, 701-722, 1984.
[1] Y. Yang, Y. Qiu and C. Lu. Automatic target classification experiments on the MSTAR SAR images. Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks, Washington: IEEE computer society, 2-7, 2005.
[2] T. Ross, S. Worrell, V. Velten, J. Mossing and M. Bryant. Standard SAR ATR evaluation experiment using the MSTAR public release data set. SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[3]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[4] C. Nadal, R. Legault and C. Y. Suen. Complementary algorithms for the recognition of totally unconstrained handwritten numerals. The Proc. 10th Int. conf. Pattern Recognition, Vol. 6 (A), 434-449, 1990.
[5] D. P. Casasent and S. Ashizawa. Synthetic aperture radar detection recognition and clutter rejection with new minimum noise and correlation energy filter. Optical Engineering, Vol. 36(10), 2729-2736, 1997.
[6] Y. Chao and C. David. A new SVM for distorted SAR object classification. Proc. of SPIE on Optical Pattern Recognition XVI, Vol. 5816, 10-22, 2005.
[7] R. C. Gonzalez and R. E. Woods. Digital image processing. Second edition, Beijing: Publishing House of Electronics Industry, 2002.
[8] S. Musman and D. Kerr and C. Bachmann. Automatic recognition of ISAR ship images. IEEE Trans. on Aerospace and Electronic Systems, Vol. 32(4), 1392-1404, 1996.
[9] K. T. Kim, D. K. Seo and H. T. Kim. Efficient classification of ISAR images. IEEE Trans. On Antenna and Propagation, 53(5), 1611-1621, 2005.
[10] P. Mari. Content-based image retrieval using shape and texture attributes. Tampere University: Department of Electrical Engineering Institute of Signal Processing, 2002.
[11] D. F. C. Luciano and M. C. J. Roberto. Shape analysis and classification: theory and practice. London: CRC Press, 2001.
[12] L. Xu, A. Kryzak and C. V. Suen. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. on Systems, Man, and Cybernetics, Vol. 22 (3), 418-435, 1992.
[1] R. O. Duda, P. E. Hart and D. G. Stork. Pattern classification. Second Edition. New York: Wiley, 2004.
[2] J. Yang, D. Zhang, A. F. Frangi and J. Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[3] S. C. Chen, Y. L. Zhu, D. Q. Zhang and J. Y. Yang. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, Vol 26, 1157-1167, 2005.
[4] D. P. Casasent and S. Ashizawa. Synthetic aperture radar detection recognition and clutter rejection with new minimum noise and correlation energy filter. Optical Engineering, Vol. 36(10), 2729-2736, 1997.
[5] R. Gonzalez and R. E. Woods. Digital image processing. Second Edition, Boston, MA: Addison-Wesley Longman Publishing Company, 1992.
[6] S. Musman and D. Kerr. Automatic recognition of ISAR ship images. IEEE Trans. on Aerospace and Electronic Systems, Vol. 32(4), 1392-1404, 1996.
[7] Q. Zhao and J. C. Principe. Support vector machine for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[8]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[9]韩萍,吴仁彪,王兆华.基于KFD准则的SAR目标特征提取与识别.现代雷达, Vol. 26(7), 27-30, 2004.
[1] R. O. Duda, P. E. Hart and D. G. Stork. Pattern classification. Second Edition. New York: Wiley, 2004.
[2] S. C. Chen, Y. L. Zhu, D. Q. Zhang and J. Y. Yang. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, Vol. 26, 1157-1167, 2005.
[3] M. Li and B. Yuan. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, 527-532, 2005.
[4] H. L. Xiong, M. N. S. Swamy and M. O. Ahmad. Two-dimensional FLD for face recognition. Pattern Recognition, Vol. 38, 1121-1124, 2005.
[5] J. Yang, D. Zhang, A. F. Frangi and J. Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[6] T. Ross, S. Worrell, V. Velten, J. Mossing and M. Bryant. Standard SAR ATR evaluation experiment using the MSTAR public release data set. SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[7] Q. Zhao and J. C. Principe. Support vector machine for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[8]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[9] S. J. Su, Z. P. Liu, S. Todorovic and J. Li. Adaptive boosting for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 43(1), 112-125, 2007.
[1] P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19(7), 711-720, 1997.
[2] X. W. Chen and T Huang. Facial expression recognition: a clustering-based approach. Pattern Recognition Letters, Vol. 24, 1295-1302, 2003.
[3] A. Likas, N. Vlassis and J. J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, Vol. 36, 451-461, 2003.
[4] T. Ross, S. Worrell, V. Velten, J. Mossing and M. Bryant. Standard SAR ATR evaluation experiment using the MSTAR public release data set. SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[5] Bryant M L and Garber F D. SVM classifier applied to the MSTAR public data set. Proc. of SPIE on Synthetic Aperture Radar Imagery VI, Vol. 3721, 355-360, 1999.
[6] Q. Zhao and J. C. Principe. Support vector machines for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[7] Y. Chao and C. David. A new SVM for distorted SAR object classification. Proc. of SPIE on Optical Pattern Recognition XVI, Vol. 5816, 10-22, 2005.
[8] L. D. Ramamoorthy and D. P. Casasent. Classification and rejection of MSTAR data. Proc. of SPIE on Optical Pattern Recognition XV, Vol. 5437, 265-276, 2004.
[9] M. L. Bryant. Target signature manifold methods applied to the MSTAR database: preliminary results. Proc. of SPIE on Synthetic Aperture Radar Imagery VIII, Vol. 4382, 389-394, 2001.
[10] R. Patnaik and D. Casasent. MSTAR object classification and confuser and clutter rejection using Minace filters. Proc. of SPIE on Automatic Target Recognition XVI, Vol. 6234, 1-13, 2006.
[11] Y. J. Sun, Z. P. Liu, S. Todorovic and J. Li. Adaptive boosting for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 43(1), 112-125, 2007.
[12]宦若虹,杨汝良,岳晋.一种合成孔径雷达图像特征提取与目标识别的新方法.电子与信息学报, Vol. 30(3), 554-558, 2008.
[13]宦若虹,杨汝良,岳晋. SVM和HMM相结合的合成孔径雷达图像目标识别.系统工程与电子技术, Vol. 30(3), 447-451, 2008.
[14]宦若虹,杨汝良.基于ICA和SVM的SAR图像特征提取与目标识别.计算机工程, Vol. 34(13), 24-28, 2008.
[15] L. S. Daniel and J. Y. Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18(8), 831-836, 1996.
[16]胡利平,刘宏伟,吴顺君.基于两级2DPCA的SAR目标特征提取及识别.电子与信息学报, Vol. 30(7), 1723-1726, 2008.
[1] X. W. Chen and T Huang. Facial expression recognition: a clustering-based approach. Pattern Recognition Letters, Vol. 24, 1295-1302, 2003.
[2] M. Zhu and A. M. Martinez. Subclass discriminant analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28(8), 1274-1286, 2006.
[3]宋枫溪,程科,杨靖宇等.最大散度差和大间隔线性投影与支持向量机.自动化学报, Vol. 30(6), 890-896, 2004.
[4]刘永俊,陈才扣.最大散度差鉴别分析及人脸识别.计算机工程与应用, Vol. 34, 208-210, 2006.
[5] Song F X, Zhang D, Mei D Y, et al. A multiple maximum scatter difference discriminant criterion for facial feature extraction. IEEE Trans. on System, Man, and Cybernetic-Part B: Cybernetics, Vol. 37(6), 1599-1606, 2007.
[6] D. Q. Zhang, Z. H. Zhou, and S. C. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, Vol. 39 (1), 140-142, 2006.
[7] S. Noushath, G. H. Kumar, and P. Shivakumara. Diagonal fisher linear discriminant analysis for efficient face recognition. Neurocomputing, Vol. 69, 1711-1716, 2006.
[8] A. Likas, N. Vlassis and J. J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, Vol. 36, 451-461, 2003.
[9]胡利平,刘宏伟,吴顺君.一种新的SAR图像目标识别预处理方法.西安电子科技大学学报, Vol. 34(5), 733-737, 2007.
[10] Q. Zhao and J. C. Principle. Support vector machine for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[11] Sun Y. J., Liu Z. P., Todorovic S., and Li J. Adaptive boosting for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, 43(1), 112-125, 2007.
[12] A. M. Mart??nez and A. C. Kak. PCA versus LDA. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 23(2), 228-233, 2001.
[13] V. Belhumeur, J. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19 (7), 711-720, 1997.
[14] D. L. Swets, and J. Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18 (8), 831-836, 1996.
[15] J. Yang, D. Zhang, A. F. Frangi, et al. Two-dimensional PCA: a new approach toappearance-based face representation and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[16] M. Li and B. Yuan. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, 527-532, 2005.
[2]黄培康,殷红成,许小剑.雷达目标特性分析.北京:电子工业出版社, 2005.
[3]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社, 2005.
[4]张澄波.综合孔径雷达.北京:科学出版社, 1989.
[5]邢孟道.基于实测数据的雷达成像方法研究.博士研究生学位论文. 2002.
[6] C. A. Wiley. Synthetic aperture radar. IEEE Trans. on Aerospace and Electronic Systems, Vol. 21(3), 440-443, 1985.
[7] W. G. Carrara, R. S. Goodman, and R. M. Majewski. Spotlight synthetic aperture radar: signal processing algorithms. Boston: Artech House, 1995.
[8]马长征.雷达目标三维成像技术研究.博士研究生学位论文.西安电子科技大学. 1999.
[9]姜正林.低分辨ISAR成像及干涉技术应用研究.博士研究生学位论文.西安电子科技大学. 2001.
[10]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[11]杜兰,刘宏伟,保铮.一种基于距离-多谱勒二维联合的群目标分辨方法.电子学报, Vol. 32(6), 881-885, 2004.
[12]杜兰,保铮,邢孟道.直升机雷达回波的分析与检测.西安电子科技大学学报, Vol. 30(5), 574-579, 2003.
[13]廖学军.基于高分辨距离像的雷达目标识别.博士研究生学位论文.西安电子科技大学. 1999。
[14]裴炳南.高分辨雷达自动目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2002.
[15]刘宏伟,杜兰,袁莉,保铮.雷达高分辨距离像目标识别研究进展.电子与信息学报, Vol. 27(8), 1328-1334, 2005.
[16]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[17]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[18]陈渤.基于核方法的雷达高分辨距离像目标识别技术研究.博士研究生学位论文.西安电子科技大学. 2008.
[19] Hongwei Liu, Zheng Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel. Lecture Notes in Computer Science, Vol. 3174 (I), 531-536, 2004.
[20] Lan Du, Hongwei Liu, Zheng Bao and Mengdao Xing. Radar HRRP target recognition based on higher order spectra. IEEE Trans. on Signal Processing, Vol. 53(7), 2359-2368, 2005.
[21]余志舜,朱兆达.逆合成孔径雷达横向距离定标.电子学报, Vol. 2(6), 45-48, 1997.
[22]李强.单脉冲雷达目标三维成像与识别研究.博士研究生学位论文.西安电子科技大学. 2007.
[23] Qun Zhang and Tat Soon Yeo. Three-dimensional SAR imaging of a groundmoving target using the InISAR technique. IEEE Trans. on Geoscience and Remote Sensing, Vol. 42(9), 1818-1828, 2004.
[24]马长征,张守宏.舰船目标单脉冲雷达三维成像技术.电子科学学刊, 22(3), 385-391, 2000.
[25] Kim Kyung-Tae, Seo Dong-Kyu, and Kim Hyo-Tae. Efficient classification of ISAR images. IEEE Trans. on antennas and propagation, Vol. 53(5), 1611-1621, 2005.
[26] S. Musman, D. Kerr, and C. Bachmann. Automatic recognition of ISAR ship images. IEEE Trans. on Aerospace and Electronics Systems, Vol. 32(4), 1392-1404, 1996.
[27]黄玉琴.基于SAR图像的城市形态时空变化的研究.中国科学院研究生院(遥感应用研究所).博士研究生学位论文. 2006.
[28]付琨.高分辨率单视单极化SAR图像地物分类方法研究.国防科学技术大学.博士研究生学位论文. 2002.
[29]胡丹.基于SVM的SAR图像地物分类研究.中南大学.硕士研究生学位论文. 2008.
[30]傅斌. SAR浅海水下地形探测.中国海洋大学.博士研究生学位论文. 2005.
[31]谭衢霖.鄱阳湖湿地生态环境遥感变化监测研究.中国科学院研究生院(遥感应用研究所).博士研究生学位论文. 2002.
[32]薛浩洁. SAR图像海洋表面油膜检测方法研究.中国科学院研究生院(电子学研究所).硕士研究生学位论文. 2004.
[33]贾承丽. SAR图像道路和机场提取方法研究.国防科学技术大学.博士研究生学位论文. 2006.
[34]蒋斌. SAR图像道路提取方法研究.国防科学技术大学.硕士研究生学位论. 2004.
[35]蒋运辉. SAR图像中的道路检测技术研究.电子科技大学.硕士研究生学位论文. 2005.
[36]戴光照,张荣.高分辨率SAR图像中的桥梁识别方法研究.遥感学报, Vol. 11(2), 177-184, 2007.
[37]曹海梅.遥感图像中水上桥梁目标识别与毁伤分析研究.南京理工大学.硕士研究生学位论文. 2005.
[38]邹英.水上桥梁目标识别算法研究.南京理工大学.硕士研究生学位论文. 2006.
[39]琚春华,王伟明,王光明,姚庆栋.一种桥梁目标识别方法的研究.计算机工程, Vol. 25(6), 19-20, 1999.
[40]李海艳.极化SAR图像海面船只监测方法研究.中国科学院研究生院(海洋研究所).博士研究生学位论文. 2007.
[41]匡纲要,计科峰,粟毅,郁文贤. SAR图像自动目标识别研究.中国图象图形学报, Vol. 8(A), 1115-1120, 2003.
[42]袁礼海,宋建社,薛文通,赵伟舟. SAR图像自动目标识别系统研究与设计.计算机应用研究, Vol. 11, 249-251, 2006.
[43] T. Ross, S. Worrell, V. Velten, J. Mossing, and M. Bryant. Standard SAR ATR evaluation experiments using the MSTAR public release data set. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[44] M. Bryant, S. Worrell, and A. Dixon. MSE template size analysis for MSTAR data. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 396-405, 1998.
[45] L. M. Kaplan. Analysis of multiplicative speckle models for template-based SAR ATR. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(4), 1424-1432, 2001.
[46] L. M.Kaplan, R. Murenzi, E. Asika, and K. Namuduri. Effect of signal-to-clutter ratio on template-based ATR. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 408-419, 1998.
[47] R. Sharma and N. S. Subotic. Construction of hybrid templates from collected and simulated data for SAR ATR algorithms. Proceedings of SPIE -The International Society for Optical Engineering, Vol. 3371, 480-486, 1998.
[48] M. Bryant, S. Worrell, and S. Parker. Class separability assessments and MSE algorithm robustness. Proceedings of SPIE -The International Society for Optical Engineering, Vol. 3070, 294-305, 1997.
[49]罗峰.合成孔径雷达图像自动目标识别方法研究.四川大学.硕士研究生学位论文.
[50]韩萍,吴仁彪,蒋立辉.一种有效的SAR图像分割与目标识别方法.系统工程与电子技术, Vol. 26(6), 737-737, 2004.
[51] A. S. Stephen, Eric Keydel, et al. The use of the mean squared error matching metric in a model-based automatic target recognition system. SPIE, 1998, Vol.3370, 360-368.
[52] B. Bhanu. Automatic target recognition: state of the art survey. IEEE Trans. on Aerospace and Electronic Systems, 1986, Vol. 22(4), 364-379.
[53] B. Bhanu, D. E. Dudgeon, et al. Introduction to the special issue on automatic target detection and recognition. IEEE Trans. on Image Processing, 1997, Vol. 6(1), 1-614.
[54] B. Bhanu, T. Jones. Image understanding research for automatic target recognition. IEEE Aerospace and Electronics Systems Magazine, 1993, Vol. 8(10), 15-23.
[55] J. A. Ratches, Waltes, et al. Aided and automatic target recognition based upon sensory inputs from images forming systems. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, Vol. 19(9), 1004-1019.
[56] G. J. Ettinger, G. A. Klanderman, et al. Probabilistic optimization approach to SAR feature matching. SPIE, 1996, Vol. 2757, 318-329.
[57] J. Wissinger, R. Washburn, and D. Morgan, et al. Search algorithms for model-based SAR ATR. SPIE, 1996, Vol. 2757, 279-293.
[58] R. K. Eric and S. W. Lee. Signature prediction for model-based automatic target recognition. SPIE, 1996, Vol.2757, 306-317.
[59] Q. Zhao and J. C. Principe. Support vector machines for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol.37(2), 643-654, 2001.
[60] M. Bryant and F. Garber. SVM classifier applied to the MSTAR public data set. SPIE, 1999, Vol.3721, 355-360.
[61]詹新光.基于支持向量机的SAR图像目标识别.西安科技大学.硕士研究生学位论文. 2008.
[62]宦若虹,杨汝良,岳晋. SVM与HMM相结合的合成孔径雷达图像目标识别.系统工程与电子技术, Vol. 30(3), 447-451, 2008.
[63]宦若虹,杨汝良.基于ICA和SVM的SAR图像特征提取与目标识别.计算机工程, Vol. 34(13), 24-28, 2008.
[64] M. Alberto. Improved multilook techniques applied to SAR and SCANSAR images. IEEE Trans. on Geoscience and Remote Sensing, Vol. 23(4), 529-534, 1991.
[65] J. S. Lee. A simple smoothing algorithm for synthetic aperture radar images. IEEE Trans. on System, Man, and Cybernetics, Vol. 13(1), 95-89, 1983.
[66] J. S. Lee. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, Vol. 25(5), 636-643, 1986.
[67] D. T. Kuan and A. A. Sawchuk. Adaptive noise smoothing filter for signal-dependent noise. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 7(2), 165-177, 1985.
[68] A. Lopes, E. Nezry and R. Touzi. Maximum a posteriori speckle and first order texture model in SAR images. Proceeding of the IGASS90. Maryland: University of Maryland, 2409-2412, 1990.
[69]付琨,匡纲要,郁文贤.基于改进相关邻域模型的SAR图像RCS重构.系统工程与电子技术, Vol.23(4), 48-53, 2001.
[70]鹂苏丹,张翠,王正志,李广侠.一种基于相关邻域模型的SAR图像迭代滤波方法.数据采集与处理, Vol.18(2), 179-184, 2003.
[71]邓炜,赵荣椿.基于小波变换的SAR图像相干斑噪声消除方法研究.信号处理, Vol. 17(1), 86-90, 2001.
[72] J. P. Greory and A. W. Robert. ATR subsystem performance measure using manual segmentation of SAR target chips. SPIE, Vol. 3721, 685-692, 1999.
[73]何壸. SAR图像分割及目标识别.西安电子科技大学.硕士研究生学位论文. 2006.
[74]彭丽娟. SAR图像去噪及分割.西安科技大学.硕士研究生学位论文. 2006.
[75]付琨,匡纲要,郁文贤.一种合成孔径雷达图像阴影和目标检测的方法.软件学报, Vol. 13(4), 818-826, 2002.
[76]李响,韩萍,吴仁彪,杨国庆.一种基于Weibull分布的SAR图像分割方法.系统工程与电子技术, Vol. 29(5), 677-679, 2007.
[77]韩萍,吴仁彪,蒋立辉.一种有效的SAR图像分割与目标识别方法.系统工程与电子技术, Vol. 26(6), 734-737, 2004.
[78]成功,毛士艺. MSTAR图像中目标和阴影的局部特征提取及分类研究.全国第十届信号与信息处理、第四届DSP应用技术联合学术会议, 2006.
[79]王义敏,安锦文,王若朴.基于局部双参数估计的SAR图像CFAR目标检测技术.火力与指挥控制, Vol. 31(4), 50-52, 2006.
[80]成功,赵巍,毛士艺.双阈值CFAR SAR目标分割方法.信号处理, Vol. 23(1), 9-14, 2007.
[81]张红蕾,宋建社,翟晓颖.一种基于二维最大熵的SAR图像自适应阈值分割算法.电光控制, Vol. 14(4), 63-65, 2007.
[82]郑宗贵,毛士艺. MSTAR图像分割算法研究.系统工程与电子技术, Vol. 24(12), 93-96, 2003.
[83]高贵,计科峰,匡纲要.基于马尔可夫随机场的SAR目标切片图像分割方法.现代雷达, Vol. 26(5), 42-45, 2004.
[84]鹂苏丹,张翠,王正志.基于马尔可夫随机场的SAR图象目标分割.中国图象图形学报, Vol. 7(A)(8), 794-799, 2002.
[85]曹兰英,夏良正,张昆辉.基于小波域MRF的SAR图像分割.东南大学学报, Vol. 34(6), 847-850, 2004.
[86]侯一民,郭雷.一种基于马尔可夫随机场的SAR图像分割新方法.电子与信息学报, Vol. 29(5), 1069-1072, 2007.
[87]张翠,郦苏丹,王正志. SAR图像目标分割与方位角估计.国防科技大学学报, Vol. 23(5), 74-78, 2001.
[88]郦苏丹,张翠,王正志. SAR图像的最优分割方法.遥感技术与应用, Vol. 16 (3), 195-199, 2001.
[89] R. A. Weisenssel, W. C. Karl, D. A. Castanon, G. J. Power and P. Douville. Markov random field segmentation methods for SAR target chips. SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, Vol. 3721, 462-473, 2000.
[90] A. Robert, W. Weisenseel, K. Clem, et al. MRF-based algorithms for segmentationof SAR images. The Proceeding of the 1998 International Conference on Image Processing. Paris: IEEE, 1998, 770-774.
[91] R. A. Weisenseel, W. C. Karl, D. A. Castanon, et al. Markov random field segmentation methods for SAR target chips. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, 1999, Vol. 3721, 462-473.
[92] E. Aitnour, S. Wang, and D. Ziou. Segmentation of small vehicle targets in SAR images. Proceedings of SPIE on Automatic Target Recognition XII, Vol. 4726, 35-45, 2002.
[93] R. Meth. Target/shadow segmentation and aspect estimation in synthetic aperture radar imagery. Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 188-196, 1998.
[94] K. S. Wilson and G. J. Power. Region growing shadow segmentation in synthetic aperture radar images. Proc of International Conference on Imaging Science, Systems, and Technology (CISST 2000), 2000.
[95] G. J. Powel and K. S. Wilson. Segmenting shadows from synthetic aperture radar imagery using edge-enhanced region growing. Proceedings of SPIE on Algorithms and Systems for Optical Information Processing IV, 2000, Vol. 4113, 106-114.
[96]王岩,梁甸农,郭汉伟.基于正则化增强的SAR图像分割方法.信号处理, Vol. 19(3), 227-229, 2003.
[97]张静,王国宏,刘福太.基于正则化方法的SAR图像分割及目标边缘检测算法.吉林大学学报, Vol. 38(1), 206-210, 2008.
[98]宦若虹,杨汝良,岳晋.水平集分割方法在合成孔径雷达图像目标识别中的应用.遥感技术与应用. Vol. 22(6), 681-684, 2007.
[99]田小林,焦李成,缑水平.基于PSO优化空间约束聚类的SAR图像分割.电子学报, Vol. 36(3), 453-457, 2008.
[100] G. J. Power. Inter-algorithmic evaluation of SAR image segmentation algorithms using multiple measures. Proceedings of SPIE on Algorithms for Synthetic Aperture Radar Imagery VIII, 2001, Vol. 4382, 330– 337.
[101] G. J. Power and A. A. S. Awwal. Optoelectronic complex inner product for evaluating quality of image segmentation. Proceedings of SPIE on PhotonicDevices and Algorithms for Computing II, 2000, Vol. 4114, 13– 19.
[102] Venkatachalam, Vidya, Hyeokho Choi, and Richard G. Baraniuk. Multiscale SAR image segmentation using wavelet-domain hidden markov tree models. Proceedings of SPIE -The International Society for Optical Engineering, 2000, Vol. 4053, 110-120.
[103] Venkatachalam, Vidya, Robert D. Nowak, Richard G. Baraniuk, and Mario A. T. Figueiredo. Unsupervised SAR image segmentation using recursive partitioning. Proceedings of SPIE -The International Society for Optical Engineering, Vol. 4053, 121-129, 2000.
[104] S. Beucher and F. Meyer. The morphological approach of segmentation: the watershed transformation. Mathematical Morphology in Image Processing. 1992.
[105]胡利平,刘宏伟,吴顺君.基于多分类器融合的SAR图像自动目标识别方法,系统工程与电子技术, Vol. 30(5), 839-842, 2008.
[106]胡利平,刘宏伟,吴顺君.一种新的SAR图像目标识别预处理方法.西安电子科技大学学报, Vol. 34(5), 733-737, 2007.
[107] M. P. Hurst and R. Mittra. Scattering center analysis via prony’s method. IEEE Trans. on Antennas and Propagation, Vol. 35, 986-989, 1987.
[108] L. C. Gerry and R. L. Moses. Attributed scattering centers for SAR ATR. IEEE Trans. on Image Processing, Vol. 6(1), 79-91, 1997.
[109] M. A. Koets. Feature extraction using attributed scattering models on SAR imagery. Proc of SPIE on Algorithms for Synthetic Aperture Radar Imagery VI, 1999, Vol. 3721, 104-115.
[110]计科峰,匡纲要,粟毅,郁文贤.一种改进的图像域SAR目标散射中心特征提取方法.电路与系统学报, Vol. 9(2), 40-44, 2004.
[111] T. D. Ross, J. J. Bradley, L. J. Hudson, and M. P. O'Connor. SAR ATR - so what's the problem?– an MSTAR perspective. Part of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI, 1999, Vol.3721, 662– 672.
[112]计科峰,匡纲要,郁文贤. SAR图像目标峰值提取与稳定性分析.现代雷达, Vol. 25(2), 15-18, 2003.
[113]高贵,计科峰,匡纲要,李德仁.高分辨SAR图像目标峰值提取及峰值稳定性分析.电子与信息学报, Vol. 27(4), 561-565, 2005.
[114]徐牧,王雪松,肖顺平.基于目标轮廓特征的SAR图像目标识别.系统工程及电子技术, Vol. 28(12), 1812-1815, 2006.
[115]王世晞,贺志国.基于PCA特征的快速SAR图像目标识别方法.国防科技大学学报, Vol. 30(3), 136-140, 2007.
[116]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR图像目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[117]韩萍,吴仁彪,王兆华.基于KFD准则的SAR目标特征提取与识别.现代雷达, Vol. 26(7), 27-30, 2004.
[118]成功,赵巍,毛士艺.基于快速提升KLDA准则的MSTAR SAR目标特征提取与识别研究.航空学报, Vol. 28(3), 667-672, 2007.
[119]徐牧,王雪松,肖顺平.基于Hough变换与目标主轴提取的SAR图像目标方位角估计方法.电子与信息学报, Vol. 29(2), 370-374, 2007.
[120]高贵,计科峰,匡纲要,李德仁.基于Radon变换的SAR图像目标统计不变特征提取研究.遥感技术与应用, Vol. 19(3), 187-192, 2004.
[121]胡利平,刘宏伟,吴顺君.基于两级2DPCA的SAR目标特征提取与识别.电子与信息学报, Vol. 30(7), 1722-1726, 2008.
[122]胡利平,刘宏伟,李丽亚,吴顺君.两向二维最大子类散度差鉴别分析及其在SAR目标识别中的应用.航空学报,已录用.
[123]胡利平,刘宏伟,尹奎英,吴顺君.基于G2DPCA的SAR目标特征提取与识别.宇航学报,已录用.
[124] Liping Hu, Jin Liu, Hongwei Liu, Bo Chen, Shunjun Wu. Automatic target recognition based on SAR images and two-stage 2DPCA features. APSAR2007. 1st Asian and Pacific Conference on Synthetic Aperture Radar, Huangshan, 801-805, 2007.
[125] Liping Hu, Hongwei Liu, Kuiying Yin, Shunjun Wu. Two-dimensional clustering-based discriminant analysis for synthetic aperture radar automatic target recognition. ISAPE2008. 8th International Symposium on Antenna, Propagation, and EM Theory, Kunming, 701-705, 2008.
[126] Liping Hu, Hongwei Liu, Shunjun Wu. Two-dimensional maximumclustering-based scatter difference discriminant analysis for synthetic aperture radar automatic target recognition. ISNN2009. 6th International Symposium on Neural Networks, Wuhan, 2009,已录用.
[127] Liping Hu, Hongwei Liu, Kuiying Yin, Shunjun Wu. Diagonal clustering-based discriminant analysis for synthetic aperture radar automatic target recognition. IET International Radar Conference, Guilin, 2009,已录用.
[128] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. Vol. 2, 121-167, 1998.
[129] R. R. Lawrence. A tutorial on hidden markov models and selected applications in speech recognition. Proceeding of the IEEE, Vol. 77(2), 257-286, 1989.
[130] E. R. Keydel, S. W. Lee, and J. T. Moore. MSTAR extended operating conditions: a tutorial. Proc of SPIE on algorithm for synthetic aperture radar imagery IV, Vol. 2757, 228-242, 1996.
[131] J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[132] S. C. Chen, Y. L. Zhu, D. Q. Zhang, and J. Y. Yang. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, Vol. 26, 1157-1167, 2005.
[133] D. Q. Zhang and Z. H. Zhou. (2D)2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing, Vol. 69, 224-231, 2005.
[134] M. Li and B. Yuan. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, 527-532, 2005.
[135] H. L. Xiong, M. N. S. Swamy, and M. O. Ahmad. Two-dimensional FLD for face recognition. Pattern Recognition, Vol. 38, 1121-1124, 2005.
[136] K. Fukunaga. Introduction to statistical pattern recognition. Boston, Academic Press Inc., 1990.
[137] X. W. Chen and T Huang. Facial expression recognition: a clustering-based approach. Pattern Recognition Letters, Vol. 24, 1295-1302, 2003.
[138] M. Zhu and A. M. Martinez. Subclass discriminant analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28(8), 1274-1286, 2006.
[139] C. Xiang and D. Huang. Feature extraction using recursive cluster-based linear discriminant with application to face recognition. IEEE Trans. image processing, Vol. 15(12), 3824-3832, 2006.
[140] A. Likas, N. Vlassis and J. J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, Vol. 36, 451-461, 2003.
[141]宋枫溪,程科,杨靖宇等.最大散度差和大间隔线性投影与支持向量机.自动化学报, Vol. 30(6), 890-896, 2004.
[142]刘永俊,陈才扣.最大散度差鉴别分析及人脸识别.计算机工程与应用, Vol. 34, 208-210, 2006.
[143] Song F X, Zhang D, Mei D Y, et al. A multiple maximum scatter difference discriminant criterion for facial feature extraction. IEEE Trans. on System, Man, and Cybernetic-Part B: Cybernetics, Vol. 37(6), 1599-1606, 2007.
[144] D. Q. Zhang, Z. H. Zhou, and S. C. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, Vol. 39 (1), 140-142, 2006.
[145] S. Noushath, G. H. Kumar, P. Shivakumara. Diagonal fisher linear discriminant analysis for efficient face recognition. Neurocomputing, Vol. 69, 1711-1716, 2006.
[1] M. Alberto. Improved multilook techniques applied to SAR and SCANSAR images. IEEE Trans. on Geoscience and Remote Sensing, Vol. 23(4), 529-534, 1991.
[2]邓炜,赵荣椿.基于小波变换的SAR图像相干斑噪声消除方法研究.信号处理, Vol. 17(1), 86-90, 2001.
[3] J. S. Lee. A simple smoothing algorithm for synthetic aperture radar images. IEEE Trans. on System, Man, and Cybernetics, Vol. 13(1), 95-89, 1983.
[4] J. S. Lee. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, Vol. 25(5), 636-643, 1986.
[5] D. T. Kuan and A. A. Sawchuk. Adaptive noise smoothing filter for signal-dependent noise. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 7(2), 165-177, 1985.
[6] A. Lopes, E. Nezry and R. Touzi. Maximum a posteriori speckle and first order texture model in SAR images. Proceeding of the IGASS90. Maryland: University of Maryland, 2409-2412, 1990.
[7] C. Oliver and S. Quegan. Understanding synthetic aperture radar images. New York: Artech House, 1998.
[8] Henri Maitre编,孙洪等译.合成孔径雷达图像处理.北京:电子工业出版社,2005.
[9]付琨,匡纲要,郁文贤.基于改进相关邻域模型的SAR图像RCS重构.系统工程与电子技术, Vol.23(4), 48-53, 2001.
[10]盛国芳,冷朝霞,焦李成.数据融合在SAR图像去噪中的应用.西安电子科技大学学报, Vol. 31(1), 91-95, 2004.
[11]C. J. Oliver. A model for non-rayleigh scattering statistics. Optical Acta., Vol. 31, 701-722, 1984.
[1] Y. Yang, Y. Qiu and C. Lu. Automatic target classification experiments on the MSTAR SAR images. Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks, Washington: IEEE computer society, 2-7, 2005.
[2] T. Ross, S. Worrell, V. Velten, J. Mossing and M. Bryant. Standard SAR ATR evaluation experiment using the MSTAR public release data set. SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[3]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[4] C. Nadal, R. Legault and C. Y. Suen. Complementary algorithms for the recognition of totally unconstrained handwritten numerals. The Proc. 10th Int. conf. Pattern Recognition, Vol. 6 (A), 434-449, 1990.
[5] D. P. Casasent and S. Ashizawa. Synthetic aperture radar detection recognition and clutter rejection with new minimum noise and correlation energy filter. Optical Engineering, Vol. 36(10), 2729-2736, 1997.
[6] Y. Chao and C. David. A new SVM for distorted SAR object classification. Proc. of SPIE on Optical Pattern Recognition XVI, Vol. 5816, 10-22, 2005.
[7] R. C. Gonzalez and R. E. Woods. Digital image processing. Second edition, Beijing: Publishing House of Electronics Industry, 2002.
[8] S. Musman and D. Kerr and C. Bachmann. Automatic recognition of ISAR ship images. IEEE Trans. on Aerospace and Electronic Systems, Vol. 32(4), 1392-1404, 1996.
[9] K. T. Kim, D. K. Seo and H. T. Kim. Efficient classification of ISAR images. IEEE Trans. On Antenna and Propagation, 53(5), 1611-1621, 2005.
[10] P. Mari. Content-based image retrieval using shape and texture attributes. Tampere University: Department of Electrical Engineering Institute of Signal Processing, 2002.
[11] D. F. C. Luciano and M. C. J. Roberto. Shape analysis and classification: theory and practice. London: CRC Press, 2001.
[12] L. Xu, A. Kryzak and C. V. Suen. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. on Systems, Man, and Cybernetics, Vol. 22 (3), 418-435, 1992.
[1] R. O. Duda, P. E. Hart and D. G. Stork. Pattern classification. Second Edition. New York: Wiley, 2004.
[2] J. Yang, D. Zhang, A. F. Frangi and J. Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[3] S. C. Chen, Y. L. Zhu, D. Q. Zhang and J. Y. Yang. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, Vol 26, 1157-1167, 2005.
[4] D. P. Casasent and S. Ashizawa. Synthetic aperture radar detection recognition and clutter rejection with new minimum noise and correlation energy filter. Optical Engineering, Vol. 36(10), 2729-2736, 1997.
[5] R. Gonzalez and R. E. Woods. Digital image processing. Second Edition, Boston, MA: Addison-Wesley Longman Publishing Company, 1992.
[6] S. Musman and D. Kerr. Automatic recognition of ISAR ship images. IEEE Trans. on Aerospace and Electronic Systems, Vol. 32(4), 1392-1404, 1996.
[7] Q. Zhao and J. C. Principe. Support vector machine for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[8]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[9]韩萍,吴仁彪,王兆华.基于KFD准则的SAR目标特征提取与识别.现代雷达, Vol. 26(7), 27-30, 2004.
[1] R. O. Duda, P. E. Hart and D. G. Stork. Pattern classification. Second Edition. New York: Wiley, 2004.
[2] S. C. Chen, Y. L. Zhu, D. Q. Zhang and J. Y. Yang. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters, Vol. 26, 1157-1167, 2005.
[3] M. Li and B. Yuan. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, 527-532, 2005.
[4] H. L. Xiong, M. N. S. Swamy and M. O. Ahmad. Two-dimensional FLD for face recognition. Pattern Recognition, Vol. 38, 1121-1124, 2005.
[5] J. Yang, D. Zhang, A. F. Frangi and J. Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[6] T. Ross, S. Worrell, V. Velten, J. Mossing and M. Bryant. Standard SAR ATR evaluation experiment using the MSTAR public release data set. SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[7] Q. Zhao and J. C. Principe. Support vector machine for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[8]韩萍,吴仁彪,王兆华,王蕴红.基于KPCA准则的SAR目标特征提取与识别.电子与信息学报, Vol. 25(10), 1297-1301, 2003.
[9] S. J. Su, Z. P. Liu, S. Todorovic and J. Li. Adaptive boosting for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 43(1), 112-125, 2007.
[1] P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19(7), 711-720, 1997.
[2] X. W. Chen and T Huang. Facial expression recognition: a clustering-based approach. Pattern Recognition Letters, Vol. 24, 1295-1302, 2003.
[3] A. Likas, N. Vlassis and J. J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, Vol. 36, 451-461, 2003.
[4] T. Ross, S. Worrell, V. Velten, J. Mossing and M. Bryant. Standard SAR ATR evaluation experiment using the MSTAR public release data set. SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-573, 1998.
[5] Bryant M L and Garber F D. SVM classifier applied to the MSTAR public data set. Proc. of SPIE on Synthetic Aperture Radar Imagery VI, Vol. 3721, 355-360, 1999.
[6] Q. Zhao and J. C. Principe. Support vector machines for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[7] Y. Chao and C. David. A new SVM for distorted SAR object classification. Proc. of SPIE on Optical Pattern Recognition XVI, Vol. 5816, 10-22, 2005.
[8] L. D. Ramamoorthy and D. P. Casasent. Classification and rejection of MSTAR data. Proc. of SPIE on Optical Pattern Recognition XV, Vol. 5437, 265-276, 2004.
[9] M. L. Bryant. Target signature manifold methods applied to the MSTAR database: preliminary results. Proc. of SPIE on Synthetic Aperture Radar Imagery VIII, Vol. 4382, 389-394, 2001.
[10] R. Patnaik and D. Casasent. MSTAR object classification and confuser and clutter rejection using Minace filters. Proc. of SPIE on Automatic Target Recognition XVI, Vol. 6234, 1-13, 2006.
[11] Y. J. Sun, Z. P. Liu, S. Todorovic and J. Li. Adaptive boosting for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 43(1), 112-125, 2007.
[12]宦若虹,杨汝良,岳晋.一种合成孔径雷达图像特征提取与目标识别的新方法.电子与信息学报, Vol. 30(3), 554-558, 2008.
[13]宦若虹,杨汝良,岳晋. SVM和HMM相结合的合成孔径雷达图像目标识别.系统工程与电子技术, Vol. 30(3), 447-451, 2008.
[14]宦若虹,杨汝良.基于ICA和SVM的SAR图像特征提取与目标识别.计算机工程, Vol. 34(13), 24-28, 2008.
[15] L. S. Daniel and J. Y. Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18(8), 831-836, 1996.
[16]胡利平,刘宏伟,吴顺君.基于两级2DPCA的SAR目标特征提取及识别.电子与信息学报, Vol. 30(7), 1723-1726, 2008.
[1] X. W. Chen and T Huang. Facial expression recognition: a clustering-based approach. Pattern Recognition Letters, Vol. 24, 1295-1302, 2003.
[2] M. Zhu and A. M. Martinez. Subclass discriminant analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28(8), 1274-1286, 2006.
[3]宋枫溪,程科,杨靖宇等.最大散度差和大间隔线性投影与支持向量机.自动化学报, Vol. 30(6), 890-896, 2004.
[4]刘永俊,陈才扣.最大散度差鉴别分析及人脸识别.计算机工程与应用, Vol. 34, 208-210, 2006.
[5] Song F X, Zhang D, Mei D Y, et al. A multiple maximum scatter difference discriminant criterion for facial feature extraction. IEEE Trans. on System, Man, and Cybernetic-Part B: Cybernetics, Vol. 37(6), 1599-1606, 2007.
[6] D. Q. Zhang, Z. H. Zhou, and S. C. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, Vol. 39 (1), 140-142, 2006.
[7] S. Noushath, G. H. Kumar, and P. Shivakumara. Diagonal fisher linear discriminant analysis for efficient face recognition. Neurocomputing, Vol. 69, 1711-1716, 2006.
[8] A. Likas, N. Vlassis and J. J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, Vol. 36, 451-461, 2003.
[9]胡利平,刘宏伟,吴顺君.一种新的SAR图像目标识别预处理方法.西安电子科技大学学报, Vol. 34(5), 733-737, 2007.
[10] Q. Zhao and J. C. Principle. Support vector machine for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, Vol. 37(2), 643-654, 2001.
[11] Sun Y. J., Liu Z. P., Todorovic S., and Li J. Adaptive boosting for SAR automatic target recognition. IEEE Trans. on Aerospace and Electronic Systems, 43(1), 112-125, 2007.
[12] A. M. Mart??nez and A. C. Kak. PCA versus LDA. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 23(2), 228-233, 2001.
[13] V. Belhumeur, J. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19 (7), 711-720, 1997.
[14] D. L. Swets, and J. Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18 (8), 831-836, 1996.
[15] J. Yang, D. Zhang, A. F. Frangi, et al. Two-dimensional PCA: a new approach toappearance-based face representation and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 26 (1), 131-137, 2004.
[16] M. Li and B. Yuan. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, 527-532, 2005.
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