极化SAR图像的分割和分类算法研究
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摘要
SAR图像中包含多种地物目标信息，图像中各类目标的准确分类，对SAR图像中地物目标信息的理解具有重要意义。特别是极化SAR，由于极化散射矩阵包含有丰富的地物信息，因此，极化SAR图像的分割和分类一直是雷达遥感应用领域的热门研究方向之一。
但是，由于自然场景的复杂性，在目前的极化SAR图像处理研究中，仍然存在着数据统计先验知识不足、特征量不能全面描述目标物理属性等问题，影响了极化SAR信息处理方法的普遍推广，如何提高分类和分割精度、鲁棒性能是当前极化SAR图像分类研究中的一个重点。
近年来，基于偏微分方程的图像分析与处理成为人们研究的焦点，本文在研究当前极化SAR图像处理中图像分割和分类领域的发展情况的基础上，重点开展以偏微分方程为基础的SAR图像分割和分类研究，主要工作和贡献如下：
1)深入分析了SAR图像的区域与边界特征，建立了参数活动轮廓模型和几何活动轮廓模型，利用特征信息定义了合理的能量泛函模型，提出了基于边界和区域信息的活动轮廓模型的图像分割水平集算法，不仅能够自然地处理边界拓扑变化，而且同时能检测图像中多个物体边缘，提高了分割性能。
2)建立了一种用于图像分类的变分模型，该模型结合正则化过程，可以较好地保持图像边缘信息，同时可以用于图像恢复。基于变分法的极化SAR图像分类方法不仅能够实现SAR图像的正确分类，克服SAR图像中相干斑噪声的影响，并且算法快速，易于实现。
3)提出了一种基于偏微分方程的多区域SAR图像分割方法，充分结合图像边缘梯度信息和多区域的统计特征信息，既克服了仅仅依靠边界梯度进行分割的缺陷，又能充分利用边界梯度信息，该方法没有引入任何附加参数，同时可以估计区域数目，使用分级分裂最小化能量函数，从而获得更理想的分割效果。
4)建立了适合于极化SAR的偏微分方程模型，利用曲线演化和水平集方法研究极化SAR图像的分割问题，并结合图像的极化信息，将极化信息作为边界演化的判定条件之一，控制边界的运动和停止，实现极化SAR图像的分割，同时有效解决水平集方法分类问题中过渡分割问题。
5)利用真实SAR数据和极化SAR数据，开展了算法仿真和试验研究，实现了SAR图像的分割和分类，实现了上述分割和分类算法的验证。
通过开展基于偏微分方程的SAR图像处理和分析，为SAR图像理解和目标识别问题提供了一个新的解决途径，也能够对基于偏微分方程的图像处理方法研究起到推动作用。
Synthetic aperture radar (SAR) instruments have been widely used in the past years for remote sensing applications such as agriculture, geology and military surveillance. Precise segmentation and classification of different types of targets in SAR images is a crucial step for SAR image understanding and interpretation. Particularly, segmentation and classification of polarimetric SAR (PolSAR) image is a hot topic in SAR applications since its polarimetric scattering matrix consists of more ground target information.
Due to the complexity of land feature, however, there are still many problems such as lack of statistical prior knowledge and insufficient description of physical property of target by the present eigenvalues. The problems are blocking the application of PolSAR processing methodology and how to improve the accuracy and robustness of segmentation or classification algorithms is generally acknowledged as a difficult problem.
Recently, more and more study has been concentrating on the partial differential equations (PDE) based image processing approaches and their applications. Based on the analysis of the existing SAR image segmentation and classification methods, this dissertation proposes to study the SAR image segmentation and classification by using curve evolution theory and level set method which are both under the framework of PDE. The primary contents and the academic contributions are as follows:
1) Following the detailed analysis of the region as well as boundary properties presented in SAR images, both parametric and geometric active contour models are presented. A more appropriate energy functional is derived by sufficiently using the image information. A level set SAR image segmentation method with joint region-boundary information is presented in this dissertation. The level set based approach has better segmentation performance and it has the ability to deal with the topology variation of active contours and to partition the multiple regions simultaneously.
2) A variational model for SAR image classification is presented. The model conserves the edge information and is suitable for image restoration since it integrates with regularization. The variational classification method which is easy to implement and has little time cost classifies the ground objects in PolSAR images with high accuracy and restrains the influence of the speckle noise.
3) A multi-region SAR image segmentation approach based on partial differential equation is proposed. The method sufficiently exploits the edge information and avoids the drawback when the segmentation merely depends on image gradient since it well integrates the gradient information and the statistical property of different regions. It needs no additional parameters and can estimate the region numbers. Hierarchical splits energy functional is used to get better segmentation results.
4) A PDE model adapted for PolSAR segmentation is presented. The segmentation of PolSAR is implemented by using curve evolution and level set method. The polarimetric information is included in the model and it is employed as a criterion of curve evolution to control the movement of active boundary The criterion guarantees the correct segmentation and avoids the over segmentation problem commonly occurs in level set method.
5) Detailed experiments are designed and different types of data, such as synthetic images, real SAR images and PolSAR images, are used to verify the performance of the segmentation and classification approaches.
In general, study on the PDE based SAR image processing method provides a new way for SAR image interpretation and target recognition and in turn is a notable promotion for the development of image processing methodology under PDE framework.
但是，由于自然场景的复杂性，在目前的极化SAR图像处理研究中，仍然存在着数据统计先验知识不足、特征量不能全面描述目标物理属性等问题，影响了极化SAR信息处理方法的普遍推广，如何提高分类和分割精度、鲁棒性能是当前极化SAR图像分类研究中的一个重点。
近年来，基于偏微分方程的图像分析与处理成为人们研究的焦点，本文在研究当前极化SAR图像处理中图像分割和分类领域的发展情况的基础上，重点开展以偏微分方程为基础的SAR图像分割和分类研究，主要工作和贡献如下：
1)深入分析了SAR图像的区域与边界特征，建立了参数活动轮廓模型和几何活动轮廓模型，利用特征信息定义了合理的能量泛函模型，提出了基于边界和区域信息的活动轮廓模型的图像分割水平集算法，不仅能够自然地处理边界拓扑变化，而且同时能检测图像中多个物体边缘，提高了分割性能。
2)建立了一种用于图像分类的变分模型，该模型结合正则化过程，可以较好地保持图像边缘信息，同时可以用于图像恢复。基于变分法的极化SAR图像分类方法不仅能够实现SAR图像的正确分类，克服SAR图像中相干斑噪声的影响，并且算法快速，易于实现。
3)提出了一种基于偏微分方程的多区域SAR图像分割方法，充分结合图像边缘梯度信息和多区域的统计特征信息，既克服了仅仅依靠边界梯度进行分割的缺陷，又能充分利用边界梯度信息，该方法没有引入任何附加参数，同时可以估计区域数目，使用分级分裂最小化能量函数，从而获得更理想的分割效果。
4)建立了适合于极化SAR的偏微分方程模型，利用曲线演化和水平集方法研究极化SAR图像的分割问题，并结合图像的极化信息，将极化信息作为边界演化的判定条件之一，控制边界的运动和停止，实现极化SAR图像的分割，同时有效解决水平集方法分类问题中过渡分割问题。
5)利用真实SAR数据和极化SAR数据，开展了算法仿真和试验研究，实现了SAR图像的分割和分类，实现了上述分割和分类算法的验证。
通过开展基于偏微分方程的SAR图像处理和分析，为SAR图像理解和目标识别问题提供了一个新的解决途径，也能够对基于偏微分方程的图像处理方法研究起到推动作用。
Synthetic aperture radar (SAR) instruments have been widely used in the past years for remote sensing applications such as agriculture, geology and military surveillance. Precise segmentation and classification of different types of targets in SAR images is a crucial step for SAR image understanding and interpretation. Particularly, segmentation and classification of polarimetric SAR (PolSAR) image is a hot topic in SAR applications since its polarimetric scattering matrix consists of more ground target information.
Due to the complexity of land feature, however, there are still many problems such as lack of statistical prior knowledge and insufficient description of physical property of target by the present eigenvalues. The problems are blocking the application of PolSAR processing methodology and how to improve the accuracy and robustness of segmentation or classification algorithms is generally acknowledged as a difficult problem.
Recently, more and more study has been concentrating on the partial differential equations (PDE) based image processing approaches and their applications. Based on the analysis of the existing SAR image segmentation and classification methods, this dissertation proposes to study the SAR image segmentation and classification by using curve evolution theory and level set method which are both under the framework of PDE. The primary contents and the academic contributions are as follows:
1) Following the detailed analysis of the region as well as boundary properties presented in SAR images, both parametric and geometric active contour models are presented. A more appropriate energy functional is derived by sufficiently using the image information. A level set SAR image segmentation method with joint region-boundary information is presented in this dissertation. The level set based approach has better segmentation performance and it has the ability to deal with the topology variation of active contours and to partition the multiple regions simultaneously.
2) A variational model for SAR image classification is presented. The model conserves the edge information and is suitable for image restoration since it integrates with regularization. The variational classification method which is easy to implement and has little time cost classifies the ground objects in PolSAR images with high accuracy and restrains the influence of the speckle noise.
3) A multi-region SAR image segmentation approach based on partial differential equation is proposed. The method sufficiently exploits the edge information and avoids the drawback when the segmentation merely depends on image gradient since it well integrates the gradient information and the statistical property of different regions. It needs no additional parameters and can estimate the region numbers. Hierarchical splits energy functional is used to get better segmentation results.
4) A PDE model adapted for PolSAR segmentation is presented. The segmentation of PolSAR is implemented by using curve evolution and level set method. The polarimetric information is included in the model and it is employed as a criterion of curve evolution to control the movement of active boundary The criterion guarantees the correct segmentation and avoids the over segmentation problem commonly occurs in level set method.
5) Detailed experiments are designed and different types of data, such as synthetic images, real SAR images and PolSAR images, are used to verify the performance of the segmentation and classification approaches.
In general, study on the PDE based SAR image processing method provides a new way for SAR image interpretation and target recognition and in turn is a notable promotion for the development of image processing methodology under PDE framework.
引文
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