【作者】 王文光；

【导师】 毛士艺；

【作者基本信息】 北京航空航天大学 ， 信号与信息处理， 2007， 博士

【摘要】 极化合成孔径雷达(POLSAR)比单极化合成孔径雷达(SAR)包含了更丰富的目标信息,已经成为国内外微波成像发展的热门方向之一。电磁波的极化对目标介电常数、物理特性、几何尺寸和取向等比较敏感,通过不同的收发天线组合测量可以得到反映目标散射特性的极化散射矩阵,这为图像理解和目标分析奠定了基础。目标特征量的提取是极化信息处理的基础,特征量提取的过程不仅是物理问题,也是数学问题,所提取的特征量不仅要反映目标的散射特性,还要有合适的数学实现。论文在较系统的研究目标特征量提取方法和特征量所包含的物理意义的基础上,对极化SAR图像降斑、极化对比增强、极化SAR图像分类以及极化SAR图像中海上舰船目标检测等方面进行了研究,并提出了一些新的概念和方法,对实测数据的处理结果表明,这些新的概念和方法对于极化SAR信息处理是有效的,本文的主要创新成果包括以下方面:1)针对基于预分类的MMSE极化降斑方法实现过程非常复杂的问题,提出了简化方案,并验证了方案的有效性;2)扩展了极化相关系数的表示方法,这种扩展增强了极化相关系数对目标的区分效果;另外还提出了极化特征图量化参数,可以从量上表征不同目标间相同极化和交叉极化的回波功率差异和极化特征图形状差异;3)定义了目标间的差异度参数,它不仅可以用于描述相干目标,还可以用于描述分布目标的差别程度。并将目标差异参数度应用于极化分类,构造了基于差异度的迭代分类方法,这种方法比传统的Wishart分类具有更好的边缘保持效果,并具有更强的鲁棒性和更易于编程实现的特点;4)针对海上舰船的对比增强,提出了自动划分舰船样本的方法,这种划分比人工划分具有更强的鲁棒性,并研究了舰船与海面、舰船与岛屿和舰船与海上其他人造目标的对比增强,针对相近散射机理目标的对比增强程度非常有限的问题,验证了基于不同极化通道间的代数运算来提高舰船和杂波对比度是可行的,为目标的对比增强研究提供了新的思路;5)将极化分类方法应用于海上舰船检测,验证了这种方法的可行性,并提出了基于特征矢量分类的舰船检测方法,得到了很好的舰船与海面、舰船与岛屿以及舰船与海上其他人造目标的分离效果,并保存了舰船尾迹等有用信息,另外还提出了针对舰船检测的目标鉴别方法,这种方法基于同极化相位差判断回波最强类是否对应舰船目标,这种鉴别方法简便易行并且是非常有效的。更多还原

【Abstract】 Compared with the single polarized SAR (synthetic aperture radar), polarimetric SAR (Pol-SAR) holds more information of targets, which is one of the most important aspects of the microwave imaging research and development. Polarimetry properties of electromagnetic (EM) waves are sensitive to the scatters permittivity, physical trait, size, orientation and so on. The polarimetric scattering matrix, which describes the backscattering properties of targets completely, can be measured by transmitting and receiving different polarized signals.The Pol-SAR processing is based on information extraction of targets, which involves both physics and mathematics, i.e. not only reflects its scattering properties, but achieves it in mathematics. In this dissertation, based on the extraction of meaningful polarization information, the polarimetric speckle reduction, polarimetric contrast enhancement, polarimetric classification and ship detection in polarimetric SAR images are systemically studied. During the study, some new concepts and methods are introduced, which are supported by the application to measured Pol-SAR data. The innovations of this dissertation are as follows:1) A simplified schem is proposed to reduce the complexity of scattering-model-based speckle filtering.2) Extend the expression of polarimetric correlation coefficient to help distinguishing targets. In addition, introduce the quantized parameters of polarimetric signature so as to show the differentiae in quantity in co-polar power, cross-polar power and shapes of polarization signatures among targets.3) Define a new parameter of difference degree between targets, which can be used for express the difference between both coherent and distributed scatters. Besides, the difference degree is applied to polarimetric classification. A new unsupervised iterative classification method based on difference degree is proposed, which is better in keeping edges, more robust and simpler to programming than Wishart classifier.4) Develop a new method to sample ships adaptively, which is used for polarimetric contrast enhancement. The adaptive method is more robust than choosing ships samples manually. Our research shows that the enhanced degree is not very notable between the similar scattering targets using polarimetric contrast enhancement. We also verify the way is feasible to enhance the contrast between ships and clutter by algebraic operation to different polarimetric channels. 5) Use the polarimetric classification into ship detection. We propose a new ship detection method base on feature vector, which can detect ships form the sea, island and other man-made objects. At the same time, we propose the method of ship discrimination based on the co-pol phase difference. The application to AIRSAR and SIR-C data shows it is simple and effective.更多还原