SAR相干斑抑制及图像压缩的小波域方法
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摘要
本文具体分析了合成孔径雷达(SAR)图像的相干斑形成机理和图像统计特性。在此基础上,结合小波变换,分别提出了先进的SAR相干斑去噪声和图像压缩的小波域方法,并最终将两者有机结合起来,在小波域内同时实现了SAR图像去噪声和压缩。为合成孔径雷达图像后处理领域开拓了一个新的方法和思路。
第二章讨论了SAR图像的相干斑形成机理与传统的抑制方法。首先介绍了合成孔径雷达的成像原理,在此基础上分析相干斑的形成原理和回波数据的统计特征,建立了相干斑噪声统计模型。基于这一模型,详细推导了传统相干斑抑制常用的空域滤波和同态滤波方法。为引入本文提出的相干斑抑制小波域算法奠定了理论基础。
第三章阐述了SAR图像压缩的信息论基础与传统压缩方法。首先从数理统计的角度分析了SAR图像的统计特性以及在图像压缩编码中的物理意义。通过对SAR图像信息熵的详尽分析,得出SAR图像由于相干斑的存在,相关性较差,适合采用基于变换编码的有损压缩方式的重要结论,为本文提出的小波变换SAR图像压缩算法奠定了理论基础。然后详细阐述了基于变换编码的传统图像压缩编码理论和编码系统各个子模块的基本原理。
前面两章讨论了SAR图像相干斑抑制和图像压缩的理论基础和传统方法,从第四章开始我们将把小波分析的思想运用到SAR图像相干斑抑制和图像压缩领域中。第四章讨论了SAR相干斑抑制的小波域方法。首先介绍了小波基本理论,分析了小波系数的空间和频率分布特点。阐述了小波阀值去噪声方法。并在此基础上考虑依据小波系数子带分布特征用定向窗口中值滤波替代阀值去噪声,创新性地提出了结合中值滤波的小波域SAR相干斑抑制方法。并给出了具体的算法实现和仿真结果。
第五章讨论了小波域方法在SAR图像压缩中的应用。首先在SPIHT算法的基础上依据小波系数的树结构特征提出了四叉树分类方法,然后详细阐述了网格编码量化原理。基于以上的分类和量化方法,创新性地提出了一种小波域内基于四叉树分类的网格编码量化方法用于SAR图像压缩,并给出了具体的方案设计、算法实现和仿真结果。
在第四章和第五章的基础上,第六章综合同态滤波、结合中值滤波的小波域相干斑抑制、四叉树分类、网格编码量化技术,最终实现了一个结合相干斑抑制的SAR图像小波域网格编码量化方案。在阐述了算法原理和实现框图之后,给出了具体的方案设计、算法实现和仿真结果。
In this dissertation, the speckle generative mechanism and image statistic characteristic in Synthetic Aperture Radar (SAR) system are analyzed concretely. On the basis of these theories, advanced methods of speckle reduction and image compression in wavelet domain are introduced respectively. Furthermore, an integrated method in wavelet domain, which could achieve both speckle reduction and image compression, is proposed at last. This method explores a novel method and idea in SAR image post-processing.
Chapter 2 discusses the generative mechanism of image speckle and conventional speckle reduction methods. First reviews the imaging theory of Synthetic Aperture Radar system. Then, Speckle generative mechanism and reflective data characteristic are analyzed and SAR speckle model are constructed. On the basis of speckle model, the traditional methods of speckle reduction, including the spatial filter and homomophic filter are introduced.
Chapter 3 reviews the information theory basis of SAR image compression and conventional compression methods. In the views of mathematical statistic,SAR image characteristic and physical meaning in image coding are analyzed firstly. By discussing SAR information entropy, it could be concluded that loss compression method based on transform coding is adaptive to SAR image compression because speckle weaken the coherence of SAR image. And then, reviews the traditional image compression theory based on transform coding and analyses kinds of sub-module in image coding system.
The former two chapters only describe theoretic basis and traditional methods of SAR speckle reduction and image compression. From chapter 4 on, the idea of wavelet analysis is applied to practical processing of SAR speckle reduction and image compression. A new method in wavelet domain, which reduces speckle of noised SAR image, is discussed in chapter 4. First, the basic theory of wavelet technique, wavelet coefficients distribution characteristic in spatial and frequency domain and wavelet domain thresholding technique are introduced. Then, according to the subbands distribution characteristic, Instead of thresholding technique, Median filter used in high frequency coefficients region windows are selected reasonably.
SAR speckle reduction in wavelet domain combined with median filter is introduced. Finally algorithm implementation and the simulation results are given.
The application of wavelet domain methods in SAR image compression is discussed in chapter 5. First, according to tree structure, quadtree classification is introduced on the basis of SPIHT algorithm. And then, Trellis Coded Quantization(TCQ) in the wavelet framework is discussed. Basis of the above methods of classification and quantization, TCQ of SAR image in wavelet domain based on quadtree classification is proposed. The corresponding project, algorithm implementation and simulation are provided too.
Basis of chapter 4 and chapter 5,the ideas of homomophic filter, median filter in wavelet domain, quadtree classification and Trellis Coded Quantization technique are integrated in chapter 6. TCQ Of SAR wavelet image combined with speckle reduction is achieved ultimately. After discussing the algorithm theory and structure, the corresponding project, algorithm implementation and simulation are given at last.
第二章讨论了SAR图像的相干斑形成机理与传统的抑制方法。首先介绍了合成孔径雷达的成像原理,在此基础上分析相干斑的形成原理和回波数据的统计特征,建立了相干斑噪声统计模型。基于这一模型,详细推导了传统相干斑抑制常用的空域滤波和同态滤波方法。为引入本文提出的相干斑抑制小波域算法奠定了理论基础。
第三章阐述了SAR图像压缩的信息论基础与传统压缩方法。首先从数理统计的角度分析了SAR图像的统计特性以及在图像压缩编码中的物理意义。通过对SAR图像信息熵的详尽分析,得出SAR图像由于相干斑的存在,相关性较差,适合采用基于变换编码的有损压缩方式的重要结论,为本文提出的小波变换SAR图像压缩算法奠定了理论基础。然后详细阐述了基于变换编码的传统图像压缩编码理论和编码系统各个子模块的基本原理。
前面两章讨论了SAR图像相干斑抑制和图像压缩的理论基础和传统方法,从第四章开始我们将把小波分析的思想运用到SAR图像相干斑抑制和图像压缩领域中。第四章讨论了SAR相干斑抑制的小波域方法。首先介绍了小波基本理论,分析了小波系数的空间和频率分布特点。阐述了小波阀值去噪声方法。并在此基础上考虑依据小波系数子带分布特征用定向窗口中值滤波替代阀值去噪声,创新性地提出了结合中值滤波的小波域SAR相干斑抑制方法。并给出了具体的算法实现和仿真结果。
第五章讨论了小波域方法在SAR图像压缩中的应用。首先在SPIHT算法的基础上依据小波系数的树结构特征提出了四叉树分类方法,然后详细阐述了网格编码量化原理。基于以上的分类和量化方法,创新性地提出了一种小波域内基于四叉树分类的网格编码量化方法用于SAR图像压缩,并给出了具体的方案设计、算法实现和仿真结果。
在第四章和第五章的基础上,第六章综合同态滤波、结合中值滤波的小波域相干斑抑制、四叉树分类、网格编码量化技术,最终实现了一个结合相干斑抑制的SAR图像小波域网格编码量化方案。在阐述了算法原理和实现框图之后,给出了具体的方案设计、算法实现和仿真结果。
In this dissertation, the speckle generative mechanism and image statistic characteristic in Synthetic Aperture Radar (SAR) system are analyzed concretely. On the basis of these theories, advanced methods of speckle reduction and image compression in wavelet domain are introduced respectively. Furthermore, an integrated method in wavelet domain, which could achieve both speckle reduction and image compression, is proposed at last. This method explores a novel method and idea in SAR image post-processing.
Chapter 2 discusses the generative mechanism of image speckle and conventional speckle reduction methods. First reviews the imaging theory of Synthetic Aperture Radar system. Then, Speckle generative mechanism and reflective data characteristic are analyzed and SAR speckle model are constructed. On the basis of speckle model, the traditional methods of speckle reduction, including the spatial filter and homomophic filter are introduced.
Chapter 3 reviews the information theory basis of SAR image compression and conventional compression methods. In the views of mathematical statistic,SAR image characteristic and physical meaning in image coding are analyzed firstly. By discussing SAR information entropy, it could be concluded that loss compression method based on transform coding is adaptive to SAR image compression because speckle weaken the coherence of SAR image. And then, reviews the traditional image compression theory based on transform coding and analyses kinds of sub-module in image coding system.
The former two chapters only describe theoretic basis and traditional methods of SAR speckle reduction and image compression. From chapter 4 on, the idea of wavelet analysis is applied to practical processing of SAR speckle reduction and image compression. A new method in wavelet domain, which reduces speckle of noised SAR image, is discussed in chapter 4. First, the basic theory of wavelet technique, wavelet coefficients distribution characteristic in spatial and frequency domain and wavelet domain thresholding technique are introduced. Then, according to the subbands distribution characteristic, Instead of thresholding technique, Median filter used in high frequency coefficients region windows are selected reasonably.
SAR speckle reduction in wavelet domain combined with median filter is introduced. Finally algorithm implementation and the simulation results are given.
The application of wavelet domain methods in SAR image compression is discussed in chapter 5. First, according to tree structure, quadtree classification is introduced on the basis of SPIHT algorithm. And then, Trellis Coded Quantization(TCQ) in the wavelet framework is discussed. Basis of the above methods of classification and quantization, TCQ of SAR image in wavelet domain based on quadtree classification is proposed. The corresponding project, algorithm implementation and simulation are provided too.
Basis of chapter 4 and chapter 5,the ideas of homomophic filter, median filter in wavelet domain, quadtree classification and Trellis Coded Quantization technique are integrated in chapter 6. TCQ Of SAR wavelet image combined with speckle reduction is achieved ultimately. After discussing the algorithm theory and structure, the corresponding project, algorithm implementation and simulation are given at last.
引文
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