基于多尺度几何变换的遥感图像处理算法研究
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摘要
遥感图像在国防和民用方面起着不可替代的作用,由于其成像机制与可见光图像的差异很大,因此研究针对该类图像的特点的处理算法尤为重要。多尺度几何分析从提出到现在已经有十几年的历史了,虽然这些方法本身具有良好的时频性能,且能很好地解决所对应方向的一些多维信号处理的问题。但是由此理论生成的多尺度几何变换还有不少的缺点而有待完善,例如,一些多尺度几何变换不具有移不变性和解析性,方向选择性较差等,而且针对遥感图像的特点不同种类的多尺度几何变换具有不同的处理效果,本文的研究主要基于目前常用的多尺度几何变换——轮廓波(Contourlet)、双树复小波(dual-tree complex wavelet, DTCWT)、剪切波(Shearlet)和超分析小波(hyperanalysis wavelet, HWT)。本文的应用领域主要是遥感图像的处理,包括SAR图像的去噪、机场异物检测、边缘检测、图像分离、遥感图像的融合。本文以多尺度几何变换为主线,针对其在遥感图像处理应用中的关键技术进行了深入系统的研究。
本文在前人的基础上从以下两个方面进行深入的研究。一方面,改进现有的多尺度几何变换的性质和创造新的具有更好性质的多尺度几何变换。另一方面,根据不同的应用场景构造不同的基于多尺度几何变换的算法将变换应用到遥感图像处理领域中。
本文的主要贡献如下：
1.基于多尺度几何变换的SAR图像去噪算法研究
针对现有多尺度几何变换缺乏方向选择性和移不变性的缺点,本文从这两方面进行了改进,针对Contourlet变换的改进包括Wavelet-Contourlet变换、复轮廓波变换、局部混合滤波,而针对Shearlet变换的改进为复Shearlet变换,最后构造了一种适合机场雷达图像去噪的移不变二维混合变换。改进的变换具有移不变性和良好的方向选择性以及稀疏性,丰富了多尺度几何变换的内容,也更有利于雷达图像的去噪。
针对SAR图像成像特点,本文基于上述的多尺度几何变换提出了几种去噪算法：一是基于改进后的Contourlet变换的SAR图像去噪算法——基于Wavelet-Contourlet变换的Cycle Spinning去噪算法、基于复Contourlet变换高斯混合去噪算法、基于局部混合滤波的去噪算法；二是基于改进后Shearlet变换的SAR图像去噪算法——基于Shearlet双变量去噪算法、基于复Shearlet变换高斯混合去噪算法、基于稀疏表示的去噪算法、基于贝叶斯收缩的去噪算法；最后则是基于移不变二维混合变换的机场雷达图像去噪。仿真结果表明了所提出的去噪算法的有效性和可靠性。针对上述的算法,本文总结了基于多尺度几何变换去噪的常用框架,并且将上述算法进行一一对比,分析他们在雷达图像去噪中的优缺点,以利于未来进一步的研究。
2.基于多尺度几何变换的SAP图像边缘检测算法研究
针对现有的多尺度几何变换在SAR图像边缘检测中没有充分地利用多尺度变换方向信息和多尺度边缘信息融合规则比较简单的缺点,本文总结了多尺度几何变换在SAR图像边缘检测中应用的典型步骤,提出了三种基于多尺度几何变换的SAP图像边缘检测算法。
第一种边缘检测算法是基于上述的局部混合滤波去噪算法构造的,本文对基于多尺度几何变换的SAR图像边缘检测算法的几个步骤进行了改进,首先改进了平滑过程,然后使用多比例模型Canny算子进行单个尺度的边缘信息检测,最后采用证据理论进行各尺度的边缘信息融合。
第二种边缘检测算法是基于稀疏去噪和最小二乘支持向量机进行边缘检测,首先使用稀疏表示进行去噪,然后采用最小二乘支持向量机进行边缘检测。
第三种边缘检测算法是基于稀疏表示去噪算法利用多尺度几何变换的方向性构造的,稀疏表示去噪是一种迭代去噪模型,本文利用形态学算子检测每次迭代的方向边缘信息,然后采用证据理论将其融合为完整的边缘。
最后本文将上述算法进行一一对比,分析它们在SAP图像边缘检测中的优缺点,以利于未来进一步的研究。
3.基于多尺度几何分离字典的图像几何分离算法研究
在研究星星轨迹时需要将天文图像中的点和曲线进行分离,而现有的算法计算复杂度太大和计算时间太长,因此本文提出了三种新的图像几何分离字典进行图像分离,其中一种是基于复Shearlet和双正交小波字典,另外一种是基于圆对称Shearlet和双正交小波字典,还有一种是基于超分析Shearlet和双正交小波字典,对于最后一种字典本文还采用了新的迭代算法进行图像几何分离。为了客观地评价各种算法的图像几何分离效果,本文提出了一种分离效果评价标准一分离度。实验结果证明了该算法的有效性,最后本文分析了上述算法的优缺点,以利于进一步的研究。
4.基于多尺度几何变换的遥感图像融合算法研究
针对当前变换域图像融合由于引入人造纹理而导致融合效果比较差的缺点,本文提出了一种基于剪切波变换和向导滤波的图像融合算法,该算法充分的利用了图像的空间连续性,从而抑制了人造纹理的产生,实验结果表明该算法不仅可以有效地提高图像融合的视觉效果,而且还具有很好的鲁棒性,可以应用到包括多聚集图像和不同类型遥感图像的图像融合中。
Remote sensing image plays an irreplaceable role in national defense and civil applications. Its imaging mechanism is different from that of visible images, so the study of remote sensing image processing using its characteristics is particularly important. Multi-scale geometric analysis has10years of history since proposed. The method has a good time-frequency performance and gives a good solution to deal with problems corresponding to multi-dimensional signal processing. However, multi-scale geometric transformations have many disadvantages that should to be improved. For example, some multi-scale geometric transformations lack shift invariance and analysis, and also have less selective direction. What's more, people will get different effects from different multi-scale geometric transformations for different characteristics of remote sensing images. The author mainly focuses on the research of multi-scale geometric transformations and its applications in remote image processing.
This dissertation mainly focuses on widely used multi-scale geometric transformations such as Contourlet, dual tree complex wavelet (DTCWT), Shearlet and hyperanalysis wavelet (HWT). Their main application area is the radar image processing, including SAR image denoising, edge detection, image separation, the airport foreign object debris detection and remote sensing image fusion.
This dissertation studies the following two aspects on the basis of our predecessors. On one hand, we improve the property of the existing multi-scale geometric transformation and create new multi-scale geometric transformation with better property. On the other hand, different multi-scale geometric transformations and models are constructed depending on the application scenario in the field of remote sensing image processing. In the course of the study, we want to construct some better multi-scale geometric transformations and propse several methods suitable for remote sensing image processing. The main contribution of this dissertation is as follows.
1. SAR image de-noising based on multi-scale geometric transformation
Since the existing multi-scale geometric transformations lack direction selection and shift invariance, this article improved several multi-scale geometric transformations with shift invariant features, including wavelet-Contourlet transform, the complex Contourlet transform, the local hybrid filter, complex Shearlet transform and shift-invariant two-dimensional hybrid transform. These improved transformations overcome the above disadvantages, enrich multi-scale geometric transformations, and they are also easier for radar image denoising.
Several denoising models based on the multi-scale geometric transformation based on improved Contourlet and Shearlet have been proposed for the imaging features of SAR image, including the cycle spinning denoising model based on Wavelet-Contourlet transform, Gaussian mixture denoising model based on the complex Contourlet transform, denoising model based on local hybrid filter, bivariate denoising model based on Shearlet, denoising model based on sparse representation, Bayesian shrinkage denoising model based on sparse representation, Gaussian mixture denoising model based on the complex Shearlet and de-noising model based on2-D shift-invariant hybrid transform for the airport radar image.
Simulation results show the validity and reliability of the proposed denoising model. In view of the above algorithms, this dissertation summarizes the common framework of denoising method based on multi-scale geometric transformation, and gives the algorithms'of comparisons, which analyze their advantages and disadvantages in radar image denoising in order to facilitate further research in the future.
2. Multi-scale geometric transformation for SAR image edge detection
Since the existing multi-scale geometric transformations in SAR image edge detection do not take full advantage of the directions information of multi-scale transform and the fusion rules of multi-scale edge information are relatively simple, this dissertation summarizes the typical steps of SAR image edge detection based on multi-scale geometric transformation, and three SAR image edge detection models based on multi-scale geometric transformation are proposed.
The first edge detection algorithm is based on local hybrid filter denoising model. We have improved several steps of the SAR image edge detection algorithm based on multi-scale geometric transformation. First, smoothing process is improved. Then the Canny operator based on ROEWA model is used for single scale edge detection. Finally, the edge of the scale information fusion using evidence theory is improved as well.
The second edge detection algorithm is based on sparse denoising and least squares support vector machine. Firstly, use sparse representation to de-noise, and then use the least squares support vector machine to detect edge.
The third edge detection algorithm is based on the sparse representation denoising model. Sparse representation denoising is an iterative denoising model. Use morphological operators to detect the direction edge information during each iteration and fuse all information to complete the whole edge using evidence theory.
Finally, the proposed algorithms are compared, and their advantages and disadvantages in the SAR image edge detection are analyzed in order to facilitate further research in the future.
3. Multi-scale geometric transformation for image separation
In the study of stars trajectory, the points and curves in astronomical images need to be separated, but the complexity of existing algorithms is very high and their computation time is too long. So in this dissertation, three new image geometric separation dictionaries for image separation are proposed. One is based on complex Shearlet and biorthogonal wavelet dictionary, another is based on circular symmetric Shearlet and biorthogonal wavelet dictionary, the last one is based on the hyperanalysis Shearlet and biorthogonal wavelet dictionary. This article also uses a new iterative algorithm for image geometric separating in the last dictionary. To get the objective evaluation of image geometry separation efficiency, this dissertation presents an evaluation of criteria separation. Experimental results show the effectiveness of the proposed algorithm. The dissertation also analyzes the advantages and disadvantages of the algorithm, in order to facilitate further research.
4. Multi-scale geometric transformation for remote sensing fusion
Since the fusion effects of the current image fusion rules in transform domain are relatively poor, which are caused by the artificial texture, the dissertation proposes a new fusion method based on Shearlet transform with guided filtering. This method greatly suppresses the artificial texture by using the spatial continuity of the image. The experimental results show that the algorithm not only effectively improves the image fusion visual effects, but also has good robustness, can be applied to the image fusions multi-gathering imagery and remote sensing images, as well as other types of image fusion.
本文在前人的基础上从以下两个方面进行深入的研究。一方面,改进现有的多尺度几何变换的性质和创造新的具有更好性质的多尺度几何变换。另一方面,根据不同的应用场景构造不同的基于多尺度几何变换的算法将变换应用到遥感图像处理领域中。
本文的主要贡献如下：
1.基于多尺度几何变换的SAR图像去噪算法研究
针对现有多尺度几何变换缺乏方向选择性和移不变性的缺点,本文从这两方面进行了改进,针对Contourlet变换的改进包括Wavelet-Contourlet变换、复轮廓波变换、局部混合滤波,而针对Shearlet变换的改进为复Shearlet变换,最后构造了一种适合机场雷达图像去噪的移不变二维混合变换。改进的变换具有移不变性和良好的方向选择性以及稀疏性,丰富了多尺度几何变换的内容,也更有利于雷达图像的去噪。
针对SAR图像成像特点,本文基于上述的多尺度几何变换提出了几种去噪算法：一是基于改进后的Contourlet变换的SAR图像去噪算法——基于Wavelet-Contourlet变换的Cycle Spinning去噪算法、基于复Contourlet变换高斯混合去噪算法、基于局部混合滤波的去噪算法；二是基于改进后Shearlet变换的SAR图像去噪算法——基于Shearlet双变量去噪算法、基于复Shearlet变换高斯混合去噪算法、基于稀疏表示的去噪算法、基于贝叶斯收缩的去噪算法；最后则是基于移不变二维混合变换的机场雷达图像去噪。仿真结果表明了所提出的去噪算法的有效性和可靠性。针对上述的算法,本文总结了基于多尺度几何变换去噪的常用框架,并且将上述算法进行一一对比,分析他们在雷达图像去噪中的优缺点,以利于未来进一步的研究。
2.基于多尺度几何变换的SAP图像边缘检测算法研究
针对现有的多尺度几何变换在SAR图像边缘检测中没有充分地利用多尺度变换方向信息和多尺度边缘信息融合规则比较简单的缺点,本文总结了多尺度几何变换在SAR图像边缘检测中应用的典型步骤,提出了三种基于多尺度几何变换的SAP图像边缘检测算法。
第一种边缘检测算法是基于上述的局部混合滤波去噪算法构造的,本文对基于多尺度几何变换的SAR图像边缘检测算法的几个步骤进行了改进,首先改进了平滑过程,然后使用多比例模型Canny算子进行单个尺度的边缘信息检测,最后采用证据理论进行各尺度的边缘信息融合。
第二种边缘检测算法是基于稀疏去噪和最小二乘支持向量机进行边缘检测,首先使用稀疏表示进行去噪,然后采用最小二乘支持向量机进行边缘检测。
第三种边缘检测算法是基于稀疏表示去噪算法利用多尺度几何变换的方向性构造的,稀疏表示去噪是一种迭代去噪模型,本文利用形态学算子检测每次迭代的方向边缘信息,然后采用证据理论将其融合为完整的边缘。
最后本文将上述算法进行一一对比,分析它们在SAP图像边缘检测中的优缺点,以利于未来进一步的研究。
3.基于多尺度几何分离字典的图像几何分离算法研究
在研究星星轨迹时需要将天文图像中的点和曲线进行分离,而现有的算法计算复杂度太大和计算时间太长,因此本文提出了三种新的图像几何分离字典进行图像分离,其中一种是基于复Shearlet和双正交小波字典,另外一种是基于圆对称Shearlet和双正交小波字典,还有一种是基于超分析Shearlet和双正交小波字典,对于最后一种字典本文还采用了新的迭代算法进行图像几何分离。为了客观地评价各种算法的图像几何分离效果,本文提出了一种分离效果评价标准一分离度。实验结果证明了该算法的有效性,最后本文分析了上述算法的优缺点,以利于进一步的研究。
4.基于多尺度几何变换的遥感图像融合算法研究
针对当前变换域图像融合由于引入人造纹理而导致融合效果比较差的缺点,本文提出了一种基于剪切波变换和向导滤波的图像融合算法,该算法充分的利用了图像的空间连续性,从而抑制了人造纹理的产生,实验结果表明该算法不仅可以有效地提高图像融合的视觉效果,而且还具有很好的鲁棒性,可以应用到包括多聚集图像和不同类型遥感图像的图像融合中。
Remote sensing image plays an irreplaceable role in national defense and civil applications. Its imaging mechanism is different from that of visible images, so the study of remote sensing image processing using its characteristics is particularly important. Multi-scale geometric analysis has10years of history since proposed. The method has a good time-frequency performance and gives a good solution to deal with problems corresponding to multi-dimensional signal processing. However, multi-scale geometric transformations have many disadvantages that should to be improved. For example, some multi-scale geometric transformations lack shift invariance and analysis, and also have less selective direction. What's more, people will get different effects from different multi-scale geometric transformations for different characteristics of remote sensing images. The author mainly focuses on the research of multi-scale geometric transformations and its applications in remote image processing.
This dissertation mainly focuses on widely used multi-scale geometric transformations such as Contourlet, dual tree complex wavelet (DTCWT), Shearlet and hyperanalysis wavelet (HWT). Their main application area is the radar image processing, including SAR image denoising, edge detection, image separation, the airport foreign object debris detection and remote sensing image fusion.
This dissertation studies the following two aspects on the basis of our predecessors. On one hand, we improve the property of the existing multi-scale geometric transformation and create new multi-scale geometric transformation with better property. On the other hand, different multi-scale geometric transformations and models are constructed depending on the application scenario in the field of remote sensing image processing. In the course of the study, we want to construct some better multi-scale geometric transformations and propse several methods suitable for remote sensing image processing. The main contribution of this dissertation is as follows.
1. SAR image de-noising based on multi-scale geometric transformation
Since the existing multi-scale geometric transformations lack direction selection and shift invariance, this article improved several multi-scale geometric transformations with shift invariant features, including wavelet-Contourlet transform, the complex Contourlet transform, the local hybrid filter, complex Shearlet transform and shift-invariant two-dimensional hybrid transform. These improved transformations overcome the above disadvantages, enrich multi-scale geometric transformations, and they are also easier for radar image denoising.
Several denoising models based on the multi-scale geometric transformation based on improved Contourlet and Shearlet have been proposed for the imaging features of SAR image, including the cycle spinning denoising model based on Wavelet-Contourlet transform, Gaussian mixture denoising model based on the complex Contourlet transform, denoising model based on local hybrid filter, bivariate denoising model based on Shearlet, denoising model based on sparse representation, Bayesian shrinkage denoising model based on sparse representation, Gaussian mixture denoising model based on the complex Shearlet and de-noising model based on2-D shift-invariant hybrid transform for the airport radar image.
Simulation results show the validity and reliability of the proposed denoising model. In view of the above algorithms, this dissertation summarizes the common framework of denoising method based on multi-scale geometric transformation, and gives the algorithms'of comparisons, which analyze their advantages and disadvantages in radar image denoising in order to facilitate further research in the future.
2. Multi-scale geometric transformation for SAR image edge detection
Since the existing multi-scale geometric transformations in SAR image edge detection do not take full advantage of the directions information of multi-scale transform and the fusion rules of multi-scale edge information are relatively simple, this dissertation summarizes the typical steps of SAR image edge detection based on multi-scale geometric transformation, and three SAR image edge detection models based on multi-scale geometric transformation are proposed.
The first edge detection algorithm is based on local hybrid filter denoising model. We have improved several steps of the SAR image edge detection algorithm based on multi-scale geometric transformation. First, smoothing process is improved. Then the Canny operator based on ROEWA model is used for single scale edge detection. Finally, the edge of the scale information fusion using evidence theory is improved as well.
The second edge detection algorithm is based on sparse denoising and least squares support vector machine. Firstly, use sparse representation to de-noise, and then use the least squares support vector machine to detect edge.
The third edge detection algorithm is based on the sparse representation denoising model. Sparse representation denoising is an iterative denoising model. Use morphological operators to detect the direction edge information during each iteration and fuse all information to complete the whole edge using evidence theory.
Finally, the proposed algorithms are compared, and their advantages and disadvantages in the SAR image edge detection are analyzed in order to facilitate further research in the future.
3. Multi-scale geometric transformation for image separation
In the study of stars trajectory, the points and curves in astronomical images need to be separated, but the complexity of existing algorithms is very high and their computation time is too long. So in this dissertation, three new image geometric separation dictionaries for image separation are proposed. One is based on complex Shearlet and biorthogonal wavelet dictionary, another is based on circular symmetric Shearlet and biorthogonal wavelet dictionary, the last one is based on the hyperanalysis Shearlet and biorthogonal wavelet dictionary. This article also uses a new iterative algorithm for image geometric separating in the last dictionary. To get the objective evaluation of image geometry separation efficiency, this dissertation presents an evaluation of criteria separation. Experimental results show the effectiveness of the proposed algorithm. The dissertation also analyzes the advantages and disadvantages of the algorithm, in order to facilitate further research.
4. Multi-scale geometric transformation for remote sensing fusion
Since the fusion effects of the current image fusion rules in transform domain are relatively poor, which are caused by the artificial texture, the dissertation proposes a new fusion method based on Shearlet transform with guided filtering. This method greatly suppresses the artificial texture by using the spatial continuity of the image. The experimental results show that the algorithm not only effectively improves the image fusion visual effects, but also has good robustness, can be applied to the image fusions multi-gathering imagery and remote sensing images, as well as other types of image fusion.
引文
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