UWB SAR叶簇隐蔽目标变化检测技术研究
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摘要
超宽带合成孔径雷达（Ultra-Wide Band Synthetic Aperture Radar，UWB SAR）具有穿透叶簇对隐蔽目标进行成像探测的能力，军事应用潜力巨大。然而，由于树干强杂波干扰的影响，长期以来，基于UWB SAR图像的叶簇隐蔽目标检测一直受到高虚警概率问题的困扰。针对此一问题，本文对基于多时相UWB SAR图像的叶簇隐蔽目标变化检测技术进行了系统深入的研究，以期利用树干杂波在不同时相UWB SAR图像中具有很强相关性的特点，采用变化检测技术对其进行有效抑制，从而获取更好的叶簇隐蔽目标检测性能。本文的研究工作主要包括以下几方面的内容。
研究了UWB SAR叶簇隐蔽目标变化检测中的图像预处理问题。提出了一种基于点特征的图像配准算法，该算法针对UWB SAR图像往往具有较高噪声的特点，设计出一种基于多尺度Harris算子的点特征提取方法，提高了噪声环境下点特征图像配准处理的可靠性；对基于线性回归模型的SAR图像相对辐射校正方法进行了理论分析，并指出其存在的误差，在此基础上提出了一种基于双方向线性回归模型的相对辐射校正方法，提高了辐射校正的精度。
研究了UWB SAR叶簇隐蔽目标变化检测中的像素级变化检测问题。首先对理想条件下基于图像差值法的像素级变化检测与基于图像比值法的像素级变化检测进行了理论分析与比较，证明了图像差值法的优越性；而后针对实际应用中，叶簇观测区域背景后向散射强度与多时相图像相关系数同时具有快变特性的特点，对图像差值法进行改进，提出了一种基于图像分割差值法的像素级变化检测法，有效克服了上述快变特性带来的不利影响；最后对图像分割差值法进行进一步的优化，提高了算法中图像分割的速度以及杂波分布估计的精度。
研究了UWB SAR叶簇隐蔽目标变化检测中的统计分布特征变化检测问题。提出了一种基于广义Laguerre多项式的统计分布特征变化检测算法，与现有算法相比，该算法具有更高的概率密度函数估计精度，因而具有更优的变化检测性能；提出了一种基于二维Edgeworth展开式的统计分布特征变化检测算法，与现有算法相比，该算法可在一维概率密度函数比较的基础上，进一步对二维概率密度函数进行比较，因而具有更好的变化检测效果。
研究了UWB SAR叶簇隐蔽目标变化检测中的融合变化检测问题。针对现有基于支持向量数据描述（Support Vector Data Description, SVDD）的融合变化检测方法的不足，利用流行学习中的拉普拉斯特征映射（Laplacian Eigenmap，LE）算法对SVDD进行改进，并在此基础上提出了一种拉普拉斯特征映射SVDD（Laplacian Eigenmap SVDD，LE-SVDD）融合变化检测方法，与原有算法相比，该方法不仅可以从训练样本中提取分类信息，而且可以根据样本间的几何结构特性对分类判决函数进行优化，从而获得更好的融合变化检测性能。此外，针对融合变化检测实际应用中，LE-SVDD训练需要进行大规模矩阵求逆运算的特点，提出了一种基于Nystr m近似的训练方法，有效的降低了LE-SVDD训练所需运算量，提高了融合变化检测的运行速度。
论文最后在总结全文研究成果的基础上，提出了下一步的研究展望。
Ultra Wide-band Synthetic Aperture Radar (UWB SAR) can penetrate the foliageand image the concealed targets in it, which is very valuable for military applications.However, due to strong trunk clutter, foliage-concealed targets detection with UWBSAR images has been troubled with high false alarm rate problem for a long period. Forthis reason, the technique of foliage-concealed targets change detection based onMulti-temporal UWB SAR images is systematically and deeply researched in this thesis.The object of the research is to effectively suppress trunk clutter and improvefoliage-concealed detection performance by make use of strong correlation betweentrunk clutter in different temporal UWB SAR images. The main research works in thisthesis are listed as following:
The problems existing in image preprocessing of UWB SAR foliaged-concealedtarget change detection are investigated. An image registration algorithm based on pointfeature is proposed. In the algorithm, taking into account of the characteristics of highnoise in UWB SAR images, a new approach of point feature extraction is designedbased on multiscle Harris operator, which improves the reliability of image registrationoperation under high nosie situation. Then the performance of SAR image relativeradiometric normalization approach based on linear regression model is theoreticallyanalyzed, and the error existing in it is pointed out as well. After that, a new relativeradiometric normalization approach is proposed. The approach is based on bi-directionlinear regression model, and the accuracy of SAR image radiometric normalization canbe improved by it.
The problems existing in UWB SAR foliage-concealed target pixel level changedetection are investigated. Firstly, the change detection performance of image differencemethod and image ratio method are analytically compared under ideal conditions, andthe image difference method is proved to be superior to the image ratio method. Afterthat, an improved image difference change detection method based on imagesegmentation is proposed. Comparing with original difference change detection method,difference change detection method based on image segmentation can overcome theadverse influence brought by fast-fluctuating character of backscatter intensity infoliage area and correlation among multi-temproal images. Therefore, it has a betterperformance in practical application. At last, the difference change detection methodbased on image segmentation is further optimized to accelerate the speed of imagesegmentation and improve the accuracy of clutter distribution estiomation.
The problems existing in UWB SAR foliage-concealed change detection based onstatistical distribution feature are investigated. A new statistical distribution featurechange detection algorithm based on generalized Laguerre polynomial is proposed. Comparing with existing algorithm, the algorithm based on generalized Laguerrepolynomial has a higher estimation accuray of probability density function. Therefore, ithas a better change detection performance. Besides that, a new statistical distributionfeature change detection algorithm based on bi-dimensional Edgeworth expansion isalso proposed. In existing algorithm, only one-dimensional probability density functionscomparision is implementd. However, in the new algorithm based on bi-dimensionalEdgeworth expansion, the two-dimensional probability density functions comparison isalso included. So a better change detection performance can be achieved by using thenew algorithm.
The problems existing in UWB SAR foliage-concealed fusion change detection areinvestigated. In order to overcome the shortages of fusion change detectiom methodbased on Support Vector Data Description (SVDD), the Laplacian Eigenmap (LE)algorithm, which belongs to manifold learning field, is applied to improve SVDD, and aLaplacian Eigenmap SVDD (LE-SVDD) fusion change detection method is proposedbased on it. Comparing with the fusion change detection method based on SVDD, theproposed method not only extracts discriminant information from training set, but alsooptimizes the discrimination function according to the geometric structurecharacteristics of samples. Therefore, it has a better change detection performance.However, in practical application, LE-SVDD training process usually needs to solve theinversion of a large-scale martrix, which need a large amount of computation. In orderto deal with this problem, a LE-SVDD training method based on Nystr mapproximation is proposed, which effectively cuts down the computation burden andimproves the speed of fusion change detection.
研究了UWB SAR叶簇隐蔽目标变化检测中的图像预处理问题。提出了一种基于点特征的图像配准算法，该算法针对UWB SAR图像往往具有较高噪声的特点，设计出一种基于多尺度Harris算子的点特征提取方法，提高了噪声环境下点特征图像配准处理的可靠性；对基于线性回归模型的SAR图像相对辐射校正方法进行了理论分析，并指出其存在的误差，在此基础上提出了一种基于双方向线性回归模型的相对辐射校正方法，提高了辐射校正的精度。
研究了UWB SAR叶簇隐蔽目标变化检测中的像素级变化检测问题。首先对理想条件下基于图像差值法的像素级变化检测与基于图像比值法的像素级变化检测进行了理论分析与比较，证明了图像差值法的优越性；而后针对实际应用中，叶簇观测区域背景后向散射强度与多时相图像相关系数同时具有快变特性的特点，对图像差值法进行改进，提出了一种基于图像分割差值法的像素级变化检测法，有效克服了上述快变特性带来的不利影响；最后对图像分割差值法进行进一步的优化，提高了算法中图像分割的速度以及杂波分布估计的精度。
研究了UWB SAR叶簇隐蔽目标变化检测中的统计分布特征变化检测问题。提出了一种基于广义Laguerre多项式的统计分布特征变化检测算法，与现有算法相比，该算法具有更高的概率密度函数估计精度，因而具有更优的变化检测性能；提出了一种基于二维Edgeworth展开式的统计分布特征变化检测算法，与现有算法相比，该算法可在一维概率密度函数比较的基础上，进一步对二维概率密度函数进行比较，因而具有更好的变化检测效果。
研究了UWB SAR叶簇隐蔽目标变化检测中的融合变化检测问题。针对现有基于支持向量数据描述（Support Vector Data Description, SVDD）的融合变化检测方法的不足，利用流行学习中的拉普拉斯特征映射（Laplacian Eigenmap，LE）算法对SVDD进行改进，并在此基础上提出了一种拉普拉斯特征映射SVDD（Laplacian Eigenmap SVDD，LE-SVDD）融合变化检测方法，与原有算法相比，该方法不仅可以从训练样本中提取分类信息，而且可以根据样本间的几何结构特性对分类判决函数进行优化，从而获得更好的融合变化检测性能。此外，针对融合变化检测实际应用中，LE-SVDD训练需要进行大规模矩阵求逆运算的特点，提出了一种基于Nystr m近似的训练方法，有效的降低了LE-SVDD训练所需运算量，提高了融合变化检测的运行速度。
论文最后在总结全文研究成果的基础上，提出了下一步的研究展望。
Ultra Wide-band Synthetic Aperture Radar (UWB SAR) can penetrate the foliageand image the concealed targets in it, which is very valuable for military applications.However, due to strong trunk clutter, foliage-concealed targets detection with UWBSAR images has been troubled with high false alarm rate problem for a long period. Forthis reason, the technique of foliage-concealed targets change detection based onMulti-temporal UWB SAR images is systematically and deeply researched in this thesis.The object of the research is to effectively suppress trunk clutter and improvefoliage-concealed detection performance by make use of strong correlation betweentrunk clutter in different temporal UWB SAR images. The main research works in thisthesis are listed as following:
The problems existing in image preprocessing of UWB SAR foliaged-concealedtarget change detection are investigated. An image registration algorithm based on pointfeature is proposed. In the algorithm, taking into account of the characteristics of highnoise in UWB SAR images, a new approach of point feature extraction is designedbased on multiscle Harris operator, which improves the reliability of image registrationoperation under high nosie situation. Then the performance of SAR image relativeradiometric normalization approach based on linear regression model is theoreticallyanalyzed, and the error existing in it is pointed out as well. After that, a new relativeradiometric normalization approach is proposed. The approach is based on bi-directionlinear regression model, and the accuracy of SAR image radiometric normalization canbe improved by it.
The problems existing in UWB SAR foliage-concealed target pixel level changedetection are investigated. Firstly, the change detection performance of image differencemethod and image ratio method are analytically compared under ideal conditions, andthe image difference method is proved to be superior to the image ratio method. Afterthat, an improved image difference change detection method based on imagesegmentation is proposed. Comparing with original difference change detection method,difference change detection method based on image segmentation can overcome theadverse influence brought by fast-fluctuating character of backscatter intensity infoliage area and correlation among multi-temproal images. Therefore, it has a betterperformance in practical application. At last, the difference change detection methodbased on image segmentation is further optimized to accelerate the speed of imagesegmentation and improve the accuracy of clutter distribution estiomation.
The problems existing in UWB SAR foliage-concealed change detection based onstatistical distribution feature are investigated. A new statistical distribution featurechange detection algorithm based on generalized Laguerre polynomial is proposed. Comparing with existing algorithm, the algorithm based on generalized Laguerrepolynomial has a higher estimation accuray of probability density function. Therefore, ithas a better change detection performance. Besides that, a new statistical distributionfeature change detection algorithm based on bi-dimensional Edgeworth expansion isalso proposed. In existing algorithm, only one-dimensional probability density functionscomparision is implementd. However, in the new algorithm based on bi-dimensionalEdgeworth expansion, the two-dimensional probability density functions comparison isalso included. So a better change detection performance can be achieved by using thenew algorithm.
The problems existing in UWB SAR foliage-concealed fusion change detection areinvestigated. In order to overcome the shortages of fusion change detectiom methodbased on Support Vector Data Description (SVDD), the Laplacian Eigenmap (LE)algorithm, which belongs to manifold learning field, is applied to improve SVDD, and aLaplacian Eigenmap SVDD (LE-SVDD) fusion change detection method is proposedbased on it. Comparing with the fusion change detection method based on SVDD, theproposed method not only extracts discriminant information from training set, but alsooptimizes the discrimination function according to the geometric structurecharacteristics of samples. Therefore, it has a better change detection performance.However, in practical application, LE-SVDD training process usually needs to solve theinversion of a large-scale martrix, which need a large amount of computation. In orderto deal with this problem, a LE-SVDD training method based on Nystr mapproximation is proposed, which effectively cuts down the computation burden andimproves the speed of fusion change detection.
引文
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