SAR图像配准及变化检测技术研究
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摘要
本文主要研究了合成孔径雷达（Synthetic Aperture Radar，SAR）图像的配准及其变化检测技术。针对SAR系统的成像特点，根据不同成像平台所获取SAR数据的差异，本文首先利用星载SAR数据（日本ALOS卫星）对基于卫星轨道参数的图像概略配准技术进行了分析，利用成像卫星其空间位置的高精度估计，提高了基准图像和重轨图像在方位向上的配准精度。对缺乏高精度平台轨道运行数据的SAR图像，则利用图像中封闭均匀区域特征提取同名特征点，实现对基准图像和待配准图像之间的自动配准。在图像配准的基础上，通过对不同的变化检测量的深入分析，针对经典阈值自动选取方法在稳健性方面的不足，提出一种更加符合SAR图像统计分布的变化检测量及对应的自动阈值选择算法。最后，本文利用运动与变化的关系，将变化检测的思想引入到可移动目标的自动检测，有效地减少了目标检测中出现的虚警。具体而言，作者主要开展了以下几个方面的工作：
（1）针对星载SAR平台的周期性和平稳性，提出一种椭圆拟合卫星轨道的方法对卫星的空间位置进行精确估计。通过将三维空间中的椭圆卫星轨道投影到二维平面，可以得到三条二维平面中的椭圆曲线，在此基础上，引入针对二维平面中椭圆的直接最小均方椭圆拟合方法（DLS-EFM），实现对椭圆几何参数的估计，从而精确估计ALOS卫星在成像过程中任意给定时刻的空间位置。
（2）如果只结合ALOS PALSAR平台的成像几何以及成像区域的地理信息，可以实现基准数据和重轨数据在距离向上1-2个像素的概略配准，而方位向上的配准误差则高达20-30个像素。针对该问题，提出利用估计得到的卫星位置，在消除成像时间差的基础上，计算重轨干涉SAR系统的干涉基线，利用基线与目标两次成像之间的几何关系，实现了基准数据和重轨数据在方位向上的概略配准，其配准误差可控制在1个像素之内，从而可显著减少下一步高精度配准的搜索空间，提高配准的效率和准确性，使重轨干涉SAR的准实时应用成为可能。
（3）SAR图像中均匀区域的提取对于实现SAR图像自动配准十分关键。针对SAR图像中均匀区域的特点，提出一种基于Frost滤波和区域生长的均匀区域提取算法。此外，本文还将基于活动轮廓模型的分割方法引入到封闭均匀区域提取中，有效地实现了SAR图像中背景区域与封闭均匀区域的分离。
（4）利用SAR图像中已提取的均匀区域，提取区域的特征点，实现SAR图像的自动配准。本文利用封闭区域的周长、面积等区域统计特性构造区域相似度量，进而实现区域之间的匹配，然后利用匹配区域的质心作为同名特征点，实现图像的自动配准。此外，本文提出一种基于多边形拟合和几何哈希理论的封闭区域匹配方法，利用闭合区域中具有代表性的角点，实现封闭均匀区域之间的匹配，然后将匹配角点作为同名特征点对图像进行配准。最后，针对经典配准精度量化评估方法的不足，引入变化检测思想，通过对配准残差数据的统计特性进行分析，实现对配准精度的准量化估计。
（5）提出一种基于SAR图像统计分布和似然比检验的变化检测量（LLI-CDM）。该变化检测量以SAR图像杂波的Gamma分布为基础，将似然比假设检验引入到变化检测。实验表明，基于该变化检测量所得到的差异图像的直方图特点鲜明：直方图曲线由两部分构成，其中高而窄的尖峰对应于占差异图像绝大部分的非变化像元部分，直方图中低且平的拖尾则代表差异图像中的变化像元部分。直方图尖峰与拖尾之间的过渡点则可看成是将未变化和变化部分分离的最佳阈值。由于变化像元其灰度值的变化量不同，导致拖尾部分不同灰度值变化的直方图随机起伏，即拖尾部分将形成一个直方图振荡区。利用直方图尖峰和拖尾的不同特点，本文提出一种相邻灰度直方图比阈值自动选择算法实现对阈值的自动选取，该阈值选取方法物理意义明确、应用简单稳健，与LLI-CDM的组合相得益彰。
（6）基于LLI-CDM得到的差异图像中未发生变化像素的灰度值高度集中，在直方图中形成一个高且窄的尖峰，而最佳阈值处于尖峰与拖尾的过渡位置，导致每一灰度级的阈值改变都将引起变化检测结果的显著差异。而SAR变化检测中应用最广的对数比变化检测量（LOG-CDM）常采用KI、EM等阈值选择算法，其阈值的自动选取往往不够准确，与最佳阈值存在显著差异，严重影响变化检测的性能。针对该问题，本文在引入马尔可夫随机场理论的基础上，提出一种融合LLI-CDM和LOG-CDM的变化检测方法，实验结果表明，该方法能够在保持这两种变化检测量的优点的同时弥补各自的不足，显著提高了变化检测的性能。
（7）拓展了变化检测技术的传统应用范围。针对某些固定场景内的可移动目标，如机场中的飞机目标、港口附近的舰船目标等，本文在引入变化检测技术的基础上，以固定场景的光学图像为先验信息，显著改善了该类可移动目标的检测性能。此外，本文还将变化检测技术引入到可移动目标的鉴别当中，针对CFAR检测后存在的大量虚警，利用相关系数和阈值分离有效鉴别并去除虚警，提高目标检测性能。
This paper mainly focused on the study of registration and change detectiontechniques with synthetic aperture radar (SAR) images. According to the characteristicsof the SAR imaging system and platform, this paper firstly uses the satellite orbit data toregister the reference image and repeat-pass image periodically acquired by ALOSPALSAR (Japan), which improves the registration precision in the azimuth direction.For the SAR images without precise imaging platform orbit data, a registration methodbased on the closed homogenous region extraction is presented. Then, after theregistration of the reference image and the input image, to overcome the shortages ofthe classical change detection measures based on intensity information from images, achange detection measure named LLI-CDM is proposed that is derived from thelikelihood ratio and the hypothesis test based on the Gamma distribution of the SARintensity image. In addition, this paper associates movements and changes andintroduces the change detection techniques into the study of moving target detection,which evidently decreases the false alarms from the traditional CFAR target detectionmethod. The work mainly includes the following aspects.
(1) Considering the periodicity and stability of space-borne SAR platform， anellipse fitting method is proposed for the estimation of the position of the satellite.Through projecting the3-D ellipse into perpendicular planes, three2-D ellipse can beacquired, which can decrease the number of parameters of the ellipse equation from tento six. Based on the direct least square ellipse fitting method (DLS-EFM), using theestimated geometrical parameters of the ellipse, the position of the satellite at any giventime can be precisely obtained.
(2) Combined with the imaging parameters of ALOS PALSAR and thegeographical information of the observational region, the reference data and therepeat-pass data can be matched with1-2pixels mis-registration error in the rangedirection, whereas the mis-registration error in the azimuth direction could be20-30pixels. Using the estimated positions of the satellite, the interferometric baseline of thereference data and the repeat-pass data can be acquired through eliminating thedifference of the imaging time. During the estimation of the interferometric baseline,using the geometric relations among the target and the first-pass and repeat-passpositions of the satellite, the registration in the azimuth direction can be obviouslyimproved with only1pixels mis-registration error. Therefore, it decreases the searchspace for the next fine registration, which is essential for the repeat-pass interferometricSAR applications and makes it possible for real-time extensions.
(3) The extraction of the closed homogeneous regions is crucial for automatic SARimage registration. A method based on Frost filter is proposed to extract the homogeneous regions using the characteristics of SAR images. In addition, the activecontour model based region segmentation is introduced to extract the homogeneousregions, which can accurately separate the homogeneous regions from the background.
(4) Using the extracted closed homogeneous regions, control points can beacquired and used to realize the automatic SAR image registration. At first, thesimilarity between regions from the reference and input images can be defined with thecombination of region’s perimeter and area, which can be used to find the best matchedregion pairs. After the matching of regions, the centroids of the regions are adopted toregister the images. In addition, a method based on polygonal fitting and geometrichashing theory is presented to match the regions. The points of the matched polygonscan be selected as the tie-points for image registration. At last, through introducing thechange detection techniques, the residual data of the reference image and the registeredimages can be used to make the quantitative evaluation of the mis-registration error.
(5) A change detection measure named LLI-CDM is proposed, which is based onthe statistical distribution of SAR image and likelihood ratio. This measure is derivedfrom the hypothesis that the SAR clutters follow the Gamma distribution, and then thehypothesis test is introduced to discriminate the changed and unchanged pixels. Theexperiments show that the histogram of the residual image using this change detectionmeasure is composed with two parts, which are corresponded to the change andunchanged pixels, respectively. The high and narrow peak represents the unchangedpixels while the low and flat tail represents the changed pixels of the residual image.The transition point between these two parts can be seemed as the optimum threshold todiscriminate the changed and unchanged pixels. Since the offsets of the changed pixelsare different, the histogram of the tail part forms an oscillating area. Using thedifferentia between the peak and tail, an automatic threshold selection method based onthe adjacent histogram ratio is proposed, which takes a clear physical signification and itis also simple and stable, making the performance of change detection fine with theLLI-CDM.
(6) The histogram of the residual image acquired from the LLI-CDM forms a veryhigh and narrow peak, which represents the unchanged pixels. The optimum thresholdfor the change detection is at the bottom of the peak, and therefore one gray-level valuechanged of the threshold results in obviously different change detection outcome. Forthe well-known logarithm change detection measure (LOG-CDM), the automaticthreshold selection methods for this measure, such as KI, EM, sometimes cannot obtainthe optimum threshold, making the change detection result unaccepted. Therefore, thispaper introduces the Markov field theory and presents a change detection methodcombining the LLI-CDM and LOG-CDM, which strengths the advantages and avoidsthe shortages of each other. The experimental results validate the performance of thenew method combined with the two change detection measures.
(7) The applications of the change detection technique has been extended. For themoving targets in a stationary scene, such as the airplanes at the airport, the ships at theharbor, this thesis introduces the change detection technique into the target detection,using the optical image of the stationary scene as the a prior knowledge, whichimproves the performance of moving target detection. In addition, change detectiontechniques can be used in the moving target discrimination. Using the correlationcoefficient and the threshold selecting method, the false alarms can be accuratelyremoved.
（1）针对星载SAR平台的周期性和平稳性，提出一种椭圆拟合卫星轨道的方法对卫星的空间位置进行精确估计。通过将三维空间中的椭圆卫星轨道投影到二维平面，可以得到三条二维平面中的椭圆曲线，在此基础上，引入针对二维平面中椭圆的直接最小均方椭圆拟合方法（DLS-EFM），实现对椭圆几何参数的估计，从而精确估计ALOS卫星在成像过程中任意给定时刻的空间位置。
（2）如果只结合ALOS PALSAR平台的成像几何以及成像区域的地理信息，可以实现基准数据和重轨数据在距离向上1-2个像素的概略配准，而方位向上的配准误差则高达20-30个像素。针对该问题，提出利用估计得到的卫星位置，在消除成像时间差的基础上，计算重轨干涉SAR系统的干涉基线，利用基线与目标两次成像之间的几何关系，实现了基准数据和重轨数据在方位向上的概略配准，其配准误差可控制在1个像素之内，从而可显著减少下一步高精度配准的搜索空间，提高配准的效率和准确性，使重轨干涉SAR的准实时应用成为可能。
（3）SAR图像中均匀区域的提取对于实现SAR图像自动配准十分关键。针对SAR图像中均匀区域的特点，提出一种基于Frost滤波和区域生长的均匀区域提取算法。此外，本文还将基于活动轮廓模型的分割方法引入到封闭均匀区域提取中，有效地实现了SAR图像中背景区域与封闭均匀区域的分离。
（4）利用SAR图像中已提取的均匀区域，提取区域的特征点，实现SAR图像的自动配准。本文利用封闭区域的周长、面积等区域统计特性构造区域相似度量，进而实现区域之间的匹配，然后利用匹配区域的质心作为同名特征点，实现图像的自动配准。此外，本文提出一种基于多边形拟合和几何哈希理论的封闭区域匹配方法，利用闭合区域中具有代表性的角点，实现封闭均匀区域之间的匹配，然后将匹配角点作为同名特征点对图像进行配准。最后，针对经典配准精度量化评估方法的不足，引入变化检测思想，通过对配准残差数据的统计特性进行分析，实现对配准精度的准量化估计。
（5）提出一种基于SAR图像统计分布和似然比检验的变化检测量（LLI-CDM）。该变化检测量以SAR图像杂波的Gamma分布为基础，将似然比假设检验引入到变化检测。实验表明，基于该变化检测量所得到的差异图像的直方图特点鲜明：直方图曲线由两部分构成，其中高而窄的尖峰对应于占差异图像绝大部分的非变化像元部分，直方图中低且平的拖尾则代表差异图像中的变化像元部分。直方图尖峰与拖尾之间的过渡点则可看成是将未变化和变化部分分离的最佳阈值。由于变化像元其灰度值的变化量不同，导致拖尾部分不同灰度值变化的直方图随机起伏，即拖尾部分将形成一个直方图振荡区。利用直方图尖峰和拖尾的不同特点，本文提出一种相邻灰度直方图比阈值自动选择算法实现对阈值的自动选取，该阈值选取方法物理意义明确、应用简单稳健，与LLI-CDM的组合相得益彰。
（6）基于LLI-CDM得到的差异图像中未发生变化像素的灰度值高度集中，在直方图中形成一个高且窄的尖峰，而最佳阈值处于尖峰与拖尾的过渡位置，导致每一灰度级的阈值改变都将引起变化检测结果的显著差异。而SAR变化检测中应用最广的对数比变化检测量（LOG-CDM）常采用KI、EM等阈值选择算法，其阈值的自动选取往往不够准确，与最佳阈值存在显著差异，严重影响变化检测的性能。针对该问题，本文在引入马尔可夫随机场理论的基础上，提出一种融合LLI-CDM和LOG-CDM的变化检测方法，实验结果表明，该方法能够在保持这两种变化检测量的优点的同时弥补各自的不足，显著提高了变化检测的性能。
（7）拓展了变化检测技术的传统应用范围。针对某些固定场景内的可移动目标，如机场中的飞机目标、港口附近的舰船目标等，本文在引入变化检测技术的基础上，以固定场景的光学图像为先验信息，显著改善了该类可移动目标的检测性能。此外，本文还将变化检测技术引入到可移动目标的鉴别当中，针对CFAR检测后存在的大量虚警，利用相关系数和阈值分离有效鉴别并去除虚警，提高目标检测性能。
This paper mainly focused on the study of registration and change detectiontechniques with synthetic aperture radar (SAR) images. According to the characteristicsof the SAR imaging system and platform, this paper firstly uses the satellite orbit data toregister the reference image and repeat-pass image periodically acquired by ALOSPALSAR (Japan), which improves the registration precision in the azimuth direction.For the SAR images without precise imaging platform orbit data, a registration methodbased on the closed homogenous region extraction is presented. Then, after theregistration of the reference image and the input image, to overcome the shortages ofthe classical change detection measures based on intensity information from images, achange detection measure named LLI-CDM is proposed that is derived from thelikelihood ratio and the hypothesis test based on the Gamma distribution of the SARintensity image. In addition, this paper associates movements and changes andintroduces the change detection techniques into the study of moving target detection,which evidently decreases the false alarms from the traditional CFAR target detectionmethod. The work mainly includes the following aspects.
(1) Considering the periodicity and stability of space-borne SAR platform， anellipse fitting method is proposed for the estimation of the position of the satellite.Through projecting the3-D ellipse into perpendicular planes, three2-D ellipse can beacquired, which can decrease the number of parameters of the ellipse equation from tento six. Based on the direct least square ellipse fitting method (DLS-EFM), using theestimated geometrical parameters of the ellipse, the position of the satellite at any giventime can be precisely obtained.
(2) Combined with the imaging parameters of ALOS PALSAR and thegeographical information of the observational region, the reference data and therepeat-pass data can be matched with1-2pixels mis-registration error in the rangedirection, whereas the mis-registration error in the azimuth direction could be20-30pixels. Using the estimated positions of the satellite, the interferometric baseline of thereference data and the repeat-pass data can be acquired through eliminating thedifference of the imaging time. During the estimation of the interferometric baseline,using the geometric relations among the target and the first-pass and repeat-passpositions of the satellite, the registration in the azimuth direction can be obviouslyimproved with only1pixels mis-registration error. Therefore, it decreases the searchspace for the next fine registration, which is essential for the repeat-pass interferometricSAR applications and makes it possible for real-time extensions.
(3) The extraction of the closed homogeneous regions is crucial for automatic SARimage registration. A method based on Frost filter is proposed to extract the homogeneous regions using the characteristics of SAR images. In addition, the activecontour model based region segmentation is introduced to extract the homogeneousregions, which can accurately separate the homogeneous regions from the background.
(4) Using the extracted closed homogeneous regions, control points can beacquired and used to realize the automatic SAR image registration. At first, thesimilarity between regions from the reference and input images can be defined with thecombination of region’s perimeter and area, which can be used to find the best matchedregion pairs. After the matching of regions, the centroids of the regions are adopted toregister the images. In addition, a method based on polygonal fitting and geometrichashing theory is presented to match the regions. The points of the matched polygonscan be selected as the tie-points for image registration. At last, through introducing thechange detection techniques, the residual data of the reference image and the registeredimages can be used to make the quantitative evaluation of the mis-registration error.
(5) A change detection measure named LLI-CDM is proposed, which is based onthe statistical distribution of SAR image and likelihood ratio. This measure is derivedfrom the hypothesis that the SAR clutters follow the Gamma distribution, and then thehypothesis test is introduced to discriminate the changed and unchanged pixels. Theexperiments show that the histogram of the residual image using this change detectionmeasure is composed with two parts, which are corresponded to the change andunchanged pixels, respectively. The high and narrow peak represents the unchangedpixels while the low and flat tail represents the changed pixels of the residual image.The transition point between these two parts can be seemed as the optimum threshold todiscriminate the changed and unchanged pixels. Since the offsets of the changed pixelsare different, the histogram of the tail part forms an oscillating area. Using thedifferentia between the peak and tail, an automatic threshold selection method based onthe adjacent histogram ratio is proposed, which takes a clear physical signification and itis also simple and stable, making the performance of change detection fine with theLLI-CDM.
(6) The histogram of the residual image acquired from the LLI-CDM forms a veryhigh and narrow peak, which represents the unchanged pixels. The optimum thresholdfor the change detection is at the bottom of the peak, and therefore one gray-level valuechanged of the threshold results in obviously different change detection outcome. Forthe well-known logarithm change detection measure (LOG-CDM), the automaticthreshold selection methods for this measure, such as KI, EM, sometimes cannot obtainthe optimum threshold, making the change detection result unaccepted. Therefore, thispaper introduces the Markov field theory and presents a change detection methodcombining the LLI-CDM and LOG-CDM, which strengths the advantages and avoidsthe shortages of each other. The experimental results validate the performance of thenew method combined with the two change detection measures.
(7) The applications of the change detection technique has been extended. For themoving targets in a stationary scene, such as the airplanes at the airport, the ships at theharbor, this thesis introduces the change detection technique into the target detection,using the optical image of the stationary scene as the a prior knowledge, whichimproves the performance of moving target detection. In addition, change detectiontechniques can be used in the moving target discrimination. Using the correlationcoefficient and the threshold selecting method, the false alarms can be accuratelyremoved.
引文
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