高分辨率极化SAR影像典型线状目标半自动提取
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摘要
合成孔径雷达(Synthetic Aperture Radar, SAR)作为主动式的微波传感器,具有全天候、全天时的优点,在军用和民用中均发挥着越来越重要的作用。随着雷达技术的发展,数据获取能力得到快速增长,SAR呈现出多波段、多极化、高分辨率的特点。与数据获取能力相比,影像解译技术的发展却相对滞后。其中线状目标提取更是影像解译的重要内容之一,如道路、输电线等典型线状目标的提取与监测在国民经济建设和国防建设中均具有重要意义,然而受到斑点噪声及目标的复杂性等因素影响,典型线状目标提取的可靠性仍面临着较大挑战。高分辨率极化SAR影像包含丰富的空间信息和完整的目标后向散射信息,为这一问题的解决提供了新的途径。如何利用高分辨率极化SAR影像中的丰富信息成为当前线状目标提取的关键问题之一
线状目标提取可分为半自动和自动两类,全自动方法是当前研究的热点,也是线状目标提取研究的最终方向,但基于线状的解译理论与技术实现全自动提取还需要一段很长的时间,而半自动提取技术能够融合计算机的精准快速计算能力和作业员的可靠识别能力,是当前条件下一种面向工程应用的较好处理方法,具有重要的实用价值,也是当前研究的热点。道路和输电线作为SAR影像上非常重要的两类典型线目标,是SAR影像线状目标提取技术领域重点研究的对象。道路由于类别多样,形态各异,且受斑点噪声影响,尤其受桥梁(天桥)、车辆等地物严重干扰等问题,提取困难；而输电线存在信杂比极低的问题,基于功率的传统方法难以奏效。
本文重点针对道路和输电线的半自动提取问题,利用高分辨率极化SAR影像,从地物散射特性和几何特性出发,深入研究了斑点噪声抑制、边缘检测、基于极化SAR影像单类分类的道路种子点提取、道路半自动跟踪和基于极化相干性的输电线检测等关键技术,提出了从预处理、初级处理等通用处理到道路和输电线等典型目标专用处理的技术路线,从而实现了典型线状目标——道路和输电线的半自动提取。主要研究内容如下：
首先针对SAR滤波既要保持线状特征又要抑制斑点噪声的问题,提出了一种保持空间结构特征和散射特性的滤波方法。该方法在空间结构丰富的区域,使用了边缘、线状和方形同质窗,能减少虚假边缘和降低真实边缘的模糊,侧重于保持空间结构的完整性；在同质区域内,滤波仅在相同散射机制的像素间进行,侧重于极化信息的保持；最后根据空间结构类型,自适应融合前后两者的滤波结果。然后通过实验定性和定量地评价了该方法在斑点噪声抑制、空间结构和散射机制保持方面的性能。
其次为克服斑点噪声的影响,充分发挥极化数据的优势,结合自适应极化最优对比增强技术(Adaptive Optimal Polarimetry Contrast Enhancement, AOPCE)和均值比(Ratio of Averaging, ROA)算子提出了一种自适应对比增强-均值比(AOPCE-ROA)边缘检测算法,能得到不同极化方式下边缘响应的最大值,并推导了其快速处理方法,最后从理论上分析了其余ROA算子的关系,并推广到了更一般的情况。同时,利用模拟数据和真实数据进行了实验,结果表明该方法利用极化合成技术,提高了极化信息利用率,更好地克服了噪声影响,得到了更好的边缘检测结果。
接着针对道路提取中只对道路类别感兴趣而很少关心其他类别的问题,将基于结构风险最小化和核思想的支持向量数据描述单类分类算法引入极化SAR影像的监督分类中,并讨论了极化SAR单类分类中的特征选择与分类参数优化问题,通过实验验证了该算法在小样本单类监督分类中的有效性。在此基础上,综合极化支持向量数据描述单类分类的结果和线状特征检测结果,提出了道路种子点指数,并用于自动提取道路种子点,实验表明该方法提高了道路跟踪中初始化的效率。
然后针对SAR影像上道路受其它地物干扰严重的问题,在自适应贝叶斯滤波框架下,提出了初始化→预测→观测→校正的道路迭代跟踪方法。该方法建立了道路跟踪的离散系统模型,利用基于线性畸变模型的加权最小二乘多视匹配方法获取道路中心观测值,并基于自适应贝叶斯滤波方法充分地利用已有观测值,通过自适应地调整步长迭代的方法实现道路跟踪。利用开发的道路跟踪原型软件系统进行了实验,结果表明改进方法充分利用了贝叶斯两种实现形式卡尔曼滤波和粒子滤波的优势,提高了道路提取的效率。
最后利用偶极子对输电线的极化散射特性进行了建模,,先分析了影响输电线后向散射的雷达入射角和极化方位角,然后根据输电线和背景杂波的方位向对称性差异,基于同极化和交叉极化的相干系数及其分布,提出了该特殊线状目标的恒虚警率检测方法。其中相干系数在Hough域进行估首,保证了样本总数足够多且都来自可能的输电线像素,提高了估首精度；对沿着方位向满足方位对称的输电线,人为引入极化方位角,使输电线与背景得到有效区分。最后使用模拟数据和机载P波段极化SAR影像验证了所提方法的有效性。
Synthetic Aperture Radar (SAR) is an active microwave sensor, and has the advantage of working all-weather and all-time. Recently, SAR has been widely applied in both military and civil. With the development of radar system, SAR remote sensing is with multi-frequency, multi-polarization, high-resolution. However, compared with the data acquisition, image interpretation was less developed. Typical linear targets are important to military, politics, and economics, such as roads and transmission lines. Therefore they become significant parts of topographic features to be interpreted. However, due to the disturbance by speckle and the complxity of linear features, it is very challenging to interpret these targets with high confidence. Furthermore, the existing methods were mainly focused on the target extraction from the low-and median-resolution images. The high-resolution polarimetric SAR images provide another way to this problem for their rich spatial details and full backscattering information from the terrain targets. Currently, to process and to analysis such information is one of key issues for linear target extraction. Based on the current theory and technologies, it would be hard to realize fully automatic linear feature recognition. Therefore, the semiautomatic method is more prefered, because it can make use of both the accurate and fast computing ability of computers and the high reliable pattern recognition ability of humankind, and has good potentials in applications.
According to the published papers, there are kinds of linear features in high-resolution polarimetric SAR images, and different methods are required to deal with different targets for their individual geometric and scattering characteristics. For example, roads, appearing as long and dark areas, varies with shapes and sizes, which are depressed by speckle seriously and disturbed by the objects besides them, over-line bridge, river bridge, cars or trucks, etc. transmission line as one of other typical linear targets, is of low signal-to-clutter ratio, and difficult to detect by traditional methods using intensity images. To solve these problems, aiming at better utilizing the scattering and geometric characteristics of interesting targets, we focused our research on key problems of the semiautomatic extraction of typical linear features from high-resolution polarimetric SAR images. After the research of the edge detector, speckle filter, road seeds extractor using one class classifier of polarimetric SAR images, semiautomatic road tracer, and transmission line detector using polarimetric coherence, and we finally constructed a semiautomatic working framework of "edge detecting—line preservative preprocessing—seed extraction—target tracing—special target detecting". The validity of the proposed method is demonstrated by a set of experiments. Overall, the research work in the paper contains some contributions as following:
Firstly, aiming at to preserve linear features, a filter fusing a spatial detail-preserving filter and polarimetric scattering preserving filter was proposed. To avoid enhancing false edges and smearing true edges, two more sub-windows--the ribbon-like and the square-shaped masks--are introduced compared with the refined Lee filter. To preserve the scattering mechanism in the homogeneous areas, the filter is carried out only for the pixels with the same scattering class. Finally, both filters are fused based on the spatial structure type. The speckle reduction, spatial preservation, and scattering preservation are evaluated by the experiments qualitatively and quantitatively.
Secondly, to make full use of polarimetric data, we proposed AOPCE-ROA for edge detection by combing adaptive polarimetry optimal contrast enhancement and ratio of averaging, and derived its fast version. The speckle is suppressed, and a maximum response of edges is obtained, because more scattering information is used by polarimetry synthesis.
Thirdly, for the tracer initialization is very important for the efficiency and accuracy, a seed extractor was proposed based on the road seed index and polarimetric Support Vector Data Description (SVDD) one-class classifier. After discussing the one-class classification, the SVDD based on the structure risk minimization and kernel tricks was first introduced to the supervised polarimetric classification, and the optimal feature vector and optimal classifier parameters were discussed. The validity of the method for small training samples was tested by experiments. Using the classification results and the line detector response, a road seed index was proposed and used to extract road seeds.
Fourthly, to reduce the disturbance of the obstacles on the road in SAR images, a semiautomatic road tracer by the "predict-measure-correct" under the framework of adaptive Bayesian filter using the profile and rectangular matching methods to obtain the road center measurement was proposed. Specifically, we firstly discussed the road tracer initialization using the seeds extracted previously. Then the observation is obtained by the weighted least square errors methods using the linear geometric and radiometric distortion model. Finally, based on the discrete road tracing system model, the road is traced using the adaptive Bayesian filter, utilizing all the observations current and before, with few supervision from users, which automatically switches the two form of Bayesian filter, the Kalman filter and Particle filter, and automatically adjust the tracing steps. It is demonstrated by the experiment using the developed prototype software that, the improved tracer makes good use of the both filter, can avoid most of the disturbance from obstacles on the road. The roads can be extracted reliably and efficiently, supervised by the user, with the observations measured by the profile and rectangular matching for the narrow and wide roads, respectively.
Finally, the scattering from transmission lines is modeled by dipoles, and the transmission lines arranged with different azimuthal angles are detected using a CFAR method of the coherence of the co-and cross-polarizations estimated by the Hough domain, based on the difference of azimuth symmetry of the transmission line the background clutter. The airborne P-band polarimetric SAR data were used to the test the validity of the method.
线状目标提取可分为半自动和自动两类,全自动方法是当前研究的热点,也是线状目标提取研究的最终方向,但基于线状的解译理论与技术实现全自动提取还需要一段很长的时间,而半自动提取技术能够融合计算机的精准快速计算能力和作业员的可靠识别能力,是当前条件下一种面向工程应用的较好处理方法,具有重要的实用价值,也是当前研究的热点。道路和输电线作为SAR影像上非常重要的两类典型线目标,是SAR影像线状目标提取技术领域重点研究的对象。道路由于类别多样,形态各异,且受斑点噪声影响,尤其受桥梁(天桥)、车辆等地物严重干扰等问题,提取困难；而输电线存在信杂比极低的问题,基于功率的传统方法难以奏效。
本文重点针对道路和输电线的半自动提取问题,利用高分辨率极化SAR影像,从地物散射特性和几何特性出发,深入研究了斑点噪声抑制、边缘检测、基于极化SAR影像单类分类的道路种子点提取、道路半自动跟踪和基于极化相干性的输电线检测等关键技术,提出了从预处理、初级处理等通用处理到道路和输电线等典型目标专用处理的技术路线,从而实现了典型线状目标——道路和输电线的半自动提取。主要研究内容如下：
首先针对SAR滤波既要保持线状特征又要抑制斑点噪声的问题,提出了一种保持空间结构特征和散射特性的滤波方法。该方法在空间结构丰富的区域,使用了边缘、线状和方形同质窗,能减少虚假边缘和降低真实边缘的模糊,侧重于保持空间结构的完整性；在同质区域内,滤波仅在相同散射机制的像素间进行,侧重于极化信息的保持；最后根据空间结构类型,自适应融合前后两者的滤波结果。然后通过实验定性和定量地评价了该方法在斑点噪声抑制、空间结构和散射机制保持方面的性能。
其次为克服斑点噪声的影响,充分发挥极化数据的优势,结合自适应极化最优对比增强技术(Adaptive Optimal Polarimetry Contrast Enhancement, AOPCE)和均值比(Ratio of Averaging, ROA)算子提出了一种自适应对比增强-均值比(AOPCE-ROA)边缘检测算法,能得到不同极化方式下边缘响应的最大值,并推导了其快速处理方法,最后从理论上分析了其余ROA算子的关系,并推广到了更一般的情况。同时,利用模拟数据和真实数据进行了实验,结果表明该方法利用极化合成技术,提高了极化信息利用率,更好地克服了噪声影响,得到了更好的边缘检测结果。
接着针对道路提取中只对道路类别感兴趣而很少关心其他类别的问题,将基于结构风险最小化和核思想的支持向量数据描述单类分类算法引入极化SAR影像的监督分类中,并讨论了极化SAR单类分类中的特征选择与分类参数优化问题,通过实验验证了该算法在小样本单类监督分类中的有效性。在此基础上,综合极化支持向量数据描述单类分类的结果和线状特征检测结果,提出了道路种子点指数,并用于自动提取道路种子点,实验表明该方法提高了道路跟踪中初始化的效率。
然后针对SAR影像上道路受其它地物干扰严重的问题,在自适应贝叶斯滤波框架下,提出了初始化→预测→观测→校正的道路迭代跟踪方法。该方法建立了道路跟踪的离散系统模型,利用基于线性畸变模型的加权最小二乘多视匹配方法获取道路中心观测值,并基于自适应贝叶斯滤波方法充分地利用已有观测值,通过自适应地调整步长迭代的方法实现道路跟踪。利用开发的道路跟踪原型软件系统进行了实验,结果表明改进方法充分利用了贝叶斯两种实现形式卡尔曼滤波和粒子滤波的优势,提高了道路提取的效率。
最后利用偶极子对输电线的极化散射特性进行了建模,,先分析了影响输电线后向散射的雷达入射角和极化方位角,然后根据输电线和背景杂波的方位向对称性差异,基于同极化和交叉极化的相干系数及其分布,提出了该特殊线状目标的恒虚警率检测方法。其中相干系数在Hough域进行估首,保证了样本总数足够多且都来自可能的输电线像素,提高了估首精度；对沿着方位向满足方位对称的输电线,人为引入极化方位角,使输电线与背景得到有效区分。最后使用模拟数据和机载P波段极化SAR影像验证了所提方法的有效性。
Synthetic Aperture Radar (SAR) is an active microwave sensor, and has the advantage of working all-weather and all-time. Recently, SAR has been widely applied in both military and civil. With the development of radar system, SAR remote sensing is with multi-frequency, multi-polarization, high-resolution. However, compared with the data acquisition, image interpretation was less developed. Typical linear targets are important to military, politics, and economics, such as roads and transmission lines. Therefore they become significant parts of topographic features to be interpreted. However, due to the disturbance by speckle and the complxity of linear features, it is very challenging to interpret these targets with high confidence. Furthermore, the existing methods were mainly focused on the target extraction from the low-and median-resolution images. The high-resolution polarimetric SAR images provide another way to this problem for their rich spatial details and full backscattering information from the terrain targets. Currently, to process and to analysis such information is one of key issues for linear target extraction. Based on the current theory and technologies, it would be hard to realize fully automatic linear feature recognition. Therefore, the semiautomatic method is more prefered, because it can make use of both the accurate and fast computing ability of computers and the high reliable pattern recognition ability of humankind, and has good potentials in applications.
According to the published papers, there are kinds of linear features in high-resolution polarimetric SAR images, and different methods are required to deal with different targets for their individual geometric and scattering characteristics. For example, roads, appearing as long and dark areas, varies with shapes and sizes, which are depressed by speckle seriously and disturbed by the objects besides them, over-line bridge, river bridge, cars or trucks, etc. transmission line as one of other typical linear targets, is of low signal-to-clutter ratio, and difficult to detect by traditional methods using intensity images. To solve these problems, aiming at better utilizing the scattering and geometric characteristics of interesting targets, we focused our research on key problems of the semiautomatic extraction of typical linear features from high-resolution polarimetric SAR images. After the research of the edge detector, speckle filter, road seeds extractor using one class classifier of polarimetric SAR images, semiautomatic road tracer, and transmission line detector using polarimetric coherence, and we finally constructed a semiautomatic working framework of "edge detecting—line preservative preprocessing—seed extraction—target tracing—special target detecting". The validity of the proposed method is demonstrated by a set of experiments. Overall, the research work in the paper contains some contributions as following:
Firstly, aiming at to preserve linear features, a filter fusing a spatial detail-preserving filter and polarimetric scattering preserving filter was proposed. To avoid enhancing false edges and smearing true edges, two more sub-windows--the ribbon-like and the square-shaped masks--are introduced compared with the refined Lee filter. To preserve the scattering mechanism in the homogeneous areas, the filter is carried out only for the pixels with the same scattering class. Finally, both filters are fused based on the spatial structure type. The speckle reduction, spatial preservation, and scattering preservation are evaluated by the experiments qualitatively and quantitatively.
Secondly, to make full use of polarimetric data, we proposed AOPCE-ROA for edge detection by combing adaptive polarimetry optimal contrast enhancement and ratio of averaging, and derived its fast version. The speckle is suppressed, and a maximum response of edges is obtained, because more scattering information is used by polarimetry synthesis.
Thirdly, for the tracer initialization is very important for the efficiency and accuracy, a seed extractor was proposed based on the road seed index and polarimetric Support Vector Data Description (SVDD) one-class classifier. After discussing the one-class classification, the SVDD based on the structure risk minimization and kernel tricks was first introduced to the supervised polarimetric classification, and the optimal feature vector and optimal classifier parameters were discussed. The validity of the method for small training samples was tested by experiments. Using the classification results and the line detector response, a road seed index was proposed and used to extract road seeds.
Fourthly, to reduce the disturbance of the obstacles on the road in SAR images, a semiautomatic road tracer by the "predict-measure-correct" under the framework of adaptive Bayesian filter using the profile and rectangular matching methods to obtain the road center measurement was proposed. Specifically, we firstly discussed the road tracer initialization using the seeds extracted previously. Then the observation is obtained by the weighted least square errors methods using the linear geometric and radiometric distortion model. Finally, based on the discrete road tracing system model, the road is traced using the adaptive Bayesian filter, utilizing all the observations current and before, with few supervision from users, which automatically switches the two form of Bayesian filter, the Kalman filter and Particle filter, and automatically adjust the tracing steps. It is demonstrated by the experiment using the developed prototype software that, the improved tracer makes good use of the both filter, can avoid most of the disturbance from obstacles on the road. The roads can be extracted reliably and efficiently, supervised by the user, with the observations measured by the profile and rectangular matching for the narrow and wide roads, respectively.
Finally, the scattering from transmission lines is modeled by dipoles, and the transmission lines arranged with different azimuthal angles are detected using a CFAR method of the coherence of the co-and cross-polarizations estimated by the Hough domain, based on the difference of azimuth symmetry of the transmission line the background clutter. The airborne P-band polarimetric SAR data were used to the test the validity of the method.
引文
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