合成孔径雷达干涉（INSAR）三维成像处理技术研究
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摘要
合成孔径雷达干涉(INSAR)三维成像技术是新近发展起来的空间观测技术，它通过两副天线同时观测或通过一副天线两次平行观测，获取地面同一景观的复图像对，根据地面各点在两幅复图像中的相位差，得出各点在两次成像中微波的路程差，从而获得地面目标的三维信息。本文在总结了INSAR三维成像技术的发展历史和现状后，根据INSAR成像技术的特点，从INSAR系统的处理过程出发，分别对INSAR系统的复图像配准、干涉纹图噪声抑制、相位解缠等关键技术进行了深入研究，同时生成了实验成像地区的相对数字高程模型(DEM)，并对影响INSAR系统测量精度的因素进行了分析。
论文将神经网络技术引入到复图像配准当中，提出了一种相关匹配和神经网络相结合的INSAR复图像配准新方法。该方法首先利用相关匹配算法实现复图像之间的粗配准，然后采用神经网络完成复图像之间的精配准，使配准精度达到亚像素级。实验结果表明该方法可应用于INSAR复图像之间的配准。
论文在分析干涉纹图中噪声来源和干涉纹图特点的基础上，提出了基于模糊神经网络的INSAR干涉纹图噪声抑制方法，该方法将干涉纹图噪声抑制分为噪声检测和噪声滤除两个步骤进行。首先采用B样条基函数作为模糊隶属函数，设计了一种基于模糊B样条基函数神经网络的噪声分类器；然后采用小波基函数作为模糊隶属函数，设计了一种基于模糊小波基函数神经网络的噪声分类器，其模糊隶属函数的形状可以实时地进行调整，从而使系统具备了自适应的特性；通过分析小脑模型神经网络的结构和不足之处，将模糊理论引入小脑模型神经网络，设计了一种基于模糊小脑模型神经网络的噪声分类器，它能够更好地反映人脑认知事物的模糊性和连续性；最后研究了一种选择掩模式自适应中值噪声滤波器。实验结果证明了所提出的三种基于模糊神经网络的噪声抑制方法不仅有效地抑制了干涉纹图中的噪声，而且很好地保留了干涉纹图中的边缘细节信息。
论文在研究INSAR相位解缠基本原理的基础上，详细分析了分枝切割相位解缠方法和最小二乘相位解缠方法，提出了一种基于最小生成树原理的相位解缠方法，阐述了该方法的设计思想和具体算法实现步骤。实验结果表明该方法可以很好地保证解缠结果的连续性。
论文利用自主开发的INSAR数据处理软件和德国欧洲空间局(ESA)提供的两对ERS-1／2串行SAR数据，进行了INSAR三维成像实验，给出了三维成像
实验结果，验证了本文所提出的各种处理方法的可靠性及有效性。论文同时对影
响INSAR系统测量精度的主要因素进行了深入地分析。
Synthetic aperture radar interferometry (INSAR) imaging technique is a new space observation technique developed in the recent years. It uses the phase difference of the radar returns in two complex synthetic aperture radar (SAR) images, of the same area acquired at separate viewpoints or times, to obtain the third dimension information of targets in the surface. In this dissertation, we first review the development of INSAR imaging technique. Then, according to the features and the data processing procedure of INSAR, the key technique including complex images coregistration, interferogram denoising and phase unwrapping are studied in detail. The relative digital elevation models (DEM) of imaging areas are generated and the main factors that influence the mapping accuracy are discussed.
In this dissertation, neural network technique is introduced into complex images coregistration, and a new method of INSAR complex images coregistration using correlation matching combined with neural network is presented. Firstly, the correlation matching algorithm is used to complex images coarse coregistration. Then, neural network is utilized to complex images fine coregistration to produce subpixel accuracy. Experimental results show that the proposed method can be used in INSAR complex images coregistration.
By analyzing the noise sources and the characteristics of interferogram, interferogram denoising methods based on fuzzy neural network are proposed. In the methods, interferogram denoising is divided into two steps: noise detection and noise filtering. Firstly, B-spline function is used as fuzzy membership function and a noise classifier using fuzzy B-spline basis function neural network is introduced. Secondly, wavelet basis function is used as fuzzy membership function and a noise classifier based on fuzzy wavelet basis function neural network is presented. In the classifier, the shape of membership function can be adjusted in real time. It endues the classifier with better capability of learning and self-adapting. Thirdly, by researching the principle of cerebellar model articulation controller (CMAC) neural network and its drawbacks, fuzzy theory is imported into CMAC and a noise classifier using fuzzy CMAC neural network is developed. It can better reflect the fuzziness and continuity
of human cerebella. Finally, a select multi-mold adaptation median noise filtering method is proposed. Experimental results show that the three proposed interferogram denoising methods can reduce interferogram noise efficiently as well as preserve edge and detail very well.
Based on the principle of INSAR phase unwrapping, branch cut algorithm and least square algorithm are researched. Then, a minimum spanning tree phase unwrapping method is presented. Idea and procedure of the method are described in detail. Experimental results show that the proposed method can keep the continuity of phase unwrapping results better.
Using the INSAR data processing software developed by ourselves, experiments have been carried out based on ERS-1/2 SAR tandem data and the results demonstrate the feasibility and validity of the methods proposed in this dissertation. The main factors that influence the INSAR mapping accuracy are also discussed in this dissertation.
论文将神经网络技术引入到复图像配准当中，提出了一种相关匹配和神经网络相结合的INSAR复图像配准新方法。该方法首先利用相关匹配算法实现复图像之间的粗配准，然后采用神经网络完成复图像之间的精配准，使配准精度达到亚像素级。实验结果表明该方法可应用于INSAR复图像之间的配准。
论文在分析干涉纹图中噪声来源和干涉纹图特点的基础上，提出了基于模糊神经网络的INSAR干涉纹图噪声抑制方法，该方法将干涉纹图噪声抑制分为噪声检测和噪声滤除两个步骤进行。首先采用B样条基函数作为模糊隶属函数，设计了一种基于模糊B样条基函数神经网络的噪声分类器；然后采用小波基函数作为模糊隶属函数，设计了一种基于模糊小波基函数神经网络的噪声分类器，其模糊隶属函数的形状可以实时地进行调整，从而使系统具备了自适应的特性；通过分析小脑模型神经网络的结构和不足之处，将模糊理论引入小脑模型神经网络，设计了一种基于模糊小脑模型神经网络的噪声分类器，它能够更好地反映人脑认知事物的模糊性和连续性；最后研究了一种选择掩模式自适应中值噪声滤波器。实验结果证明了所提出的三种基于模糊神经网络的噪声抑制方法不仅有效地抑制了干涉纹图中的噪声，而且很好地保留了干涉纹图中的边缘细节信息。
论文在研究INSAR相位解缠基本原理的基础上，详细分析了分枝切割相位解缠方法和最小二乘相位解缠方法，提出了一种基于最小生成树原理的相位解缠方法，阐述了该方法的设计思想和具体算法实现步骤。实验结果表明该方法可以很好地保证解缠结果的连续性。
论文利用自主开发的INSAR数据处理软件和德国欧洲空间局(ESA)提供的两对ERS-1／2串行SAR数据，进行了INSAR三维成像实验，给出了三维成像
实验结果，验证了本文所提出的各种处理方法的可靠性及有效性。论文同时对影
响INSAR系统测量精度的主要因素进行了深入地分析。
Synthetic aperture radar interferometry (INSAR) imaging technique is a new space observation technique developed in the recent years. It uses the phase difference of the radar returns in two complex synthetic aperture radar (SAR) images, of the same area acquired at separate viewpoints or times, to obtain the third dimension information of targets in the surface. In this dissertation, we first review the development of INSAR imaging technique. Then, according to the features and the data processing procedure of INSAR, the key technique including complex images coregistration, interferogram denoising and phase unwrapping are studied in detail. The relative digital elevation models (DEM) of imaging areas are generated and the main factors that influence the mapping accuracy are discussed.
In this dissertation, neural network technique is introduced into complex images coregistration, and a new method of INSAR complex images coregistration using correlation matching combined with neural network is presented. Firstly, the correlation matching algorithm is used to complex images coarse coregistration. Then, neural network is utilized to complex images fine coregistration to produce subpixel accuracy. Experimental results show that the proposed method can be used in INSAR complex images coregistration.
By analyzing the noise sources and the characteristics of interferogram, interferogram denoising methods based on fuzzy neural network are proposed. In the methods, interferogram denoising is divided into two steps: noise detection and noise filtering. Firstly, B-spline function is used as fuzzy membership function and a noise classifier using fuzzy B-spline basis function neural network is introduced. Secondly, wavelet basis function is used as fuzzy membership function and a noise classifier based on fuzzy wavelet basis function neural network is presented. In the classifier, the shape of membership function can be adjusted in real time. It endues the classifier with better capability of learning and self-adapting. Thirdly, by researching the principle of cerebellar model articulation controller (CMAC) neural network and its drawbacks, fuzzy theory is imported into CMAC and a noise classifier using fuzzy CMAC neural network is developed. It can better reflect the fuzziness and continuity
of human cerebella. Finally, a select multi-mold adaptation median noise filtering method is proposed. Experimental results show that the three proposed interferogram denoising methods can reduce interferogram noise efficiently as well as preserve edge and detail very well.
Based on the principle of INSAR phase unwrapping, branch cut algorithm and least square algorithm are researched. Then, a minimum spanning tree phase unwrapping method is presented. Idea and procedure of the method are described in detail. Experimental results show that the proposed method can keep the continuity of phase unwrapping results better.
Using the INSAR data processing software developed by ourselves, experiments have been carried out based on ERS-1/2 SAR tandem data and the results demonstrate the feasibility and validity of the methods proposed in this dissertation. The main factors that influence the INSAR mapping accuracy are also discussed in this dissertation.
引文
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