高分辨率SAR图像建筑物提取方法研究
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摘要
针对城区SAR图像解译应用的需求,本文研究了高分辨率SAR图像的建筑物提取技术。面临当前实际应用中对大场景高分辨率图像进行处理的迫切需求,提出建筑物提取的分层处理思路,即首先从大场景图像中检测出感兴趣的建筑区,然后在建筑区内部进行建筑物的检测,最后对检测到的建筑物进行几何信息提取。按照这一思路,在建筑物SAR图像特征分析与仿真的基础上,对建筑区检测、建筑物检测及几何信息提取技术进行了深入的研究。开展的工作主要包括以下几个方面:
(1)建筑物SAR图像的特征分析与仿真。分析了建筑区的主要散射机制和建筑物的典型成像效应及其到SAR图像上的映射关系,归纳总结了建筑物在SAR图像中的典型特征,并仿真建筑物的SAR图像对此进行了验证。这些工作为后续研究的开展奠定了基础,特别地,建筑物图像仿真还为建筑物几何信息提取阶段的研究提供了测试数据集。
(2)SAR图像中建筑区的检测。根据为建筑物检测阶段提供ROI区域的要求,以准确、高效地完成建筑区检测为目标,提出了一种基于变差函数纹理特征的SAR图像建筑区检测方法。该方法的特点与优势在于:首先,变差函数纹理特征能够刻画高分辨率SAR图像中建筑区所表现出来的很强的非相似性,易于区分图像中的建筑区与非建筑区;其次,通过分析建筑区与非建筑区变差函数曲线的规律,可以确定最适于建筑区-非建筑区两类分类的纹理间距;再次,变差函数纹理特征计算可高效完成,在理论分析其计算量的基础上,推导出了相应的快速算法,大大提高了建筑区检测方法的实用性,能够满足为后续建筑物检测提供ROI区域的应用需求。
(3)SAR图像中建筑物的检测。以完整、准确地检测到建筑区中单个建筑物目标轮廓为目标,针对现有检测方法对复杂场景以及不同建筑物图像的适用性难以保证的问题,提出一种基于标记控制分水岭变换的SAR图像建筑物检测方法。将标记控制的分水岭变换分割框架引入建筑物检测之中,可以同时利用建筑物自身的强散射、高灰度特点及其所处的黑色道路(阴影)网状结构的背景特点,二者的结合有效克服了复杂场景中相邻建筑物目标易于错误连接、而单个建筑物由于自身灰度分布不均又易于断裂成多个部分的问题。新方法可得到建筑物目标的闭合轮廓,由于采用了适于SAR图像且定位性能良好的ROEWA边缘检测器,使分割得到的建筑物轮廓也具有定位准确的特点。此外,根据SAR图像中建筑物的典型形状特征,提出一种方向相关的形状分析法,可进一步有效地排除虚警。实验结果证明新方法具有检测率高、虚警率低、定位准确等优点,是一种有效的方法,所得结果能对后续建筑物几何信息的提取提供有力支持。
(4)SAR图像中建筑物几何信息的提取。针对现有方法存在精度不高或实用性太低的问题,提出了一种基于模型与图像匹配度的建筑物几何信息提取框架。该框架一方面将建筑物模型映射到图像上,确定图像中包含有建筑物几何信息的典型特征区域如叠掩和阴影区域,另一方面利用建筑物检测阶段得到的建筑物目标轮廓等信息,通过二者的匹配度来寻求最佳的模型参数。新框架具有较强的通用性,建筑物参数化几何模型、典型特征区域及其提取方法都可以随实际应用而调整。以最常见的平顶长条形建筑物为例,讨论了该框架的一个具体应用:详细分析了在不同成像参数与模型参数条件下,将建筑物模型映射为图像中叠掩和阴影典型特征区域的方法;设计了叠掩边界匹配度和阴影边界匹配度来衡量模型与图像的匹配程度;给出了基于遗传算法的最优化求解方法,取匹配度函数最大化时的模型参数作为待求解建筑物的几何参数。仿真和实测数据实验结果表明,由于新方法引入了由建筑物几何模型到图像的精确映射关系,可确保求解的精度;而且将几何信息提取问题转化为匹配度函数最大化问题,可利用优化算法自动求解,无需人工操作,大大提高了实用性。
With the demand on urban SAR image interpretation, techniques of building extraction from high-resolution SAR images are investigated in this thesis. Aiming at dealing with high-resolution and large-scene images in practical applications, a hierarchical procedure for building extraction is proposed. First, the built-up areas are detected from the large-scene image; then, building detection is carried out in the detected built-up areas; finally, the geometrical information of the buildings is extracted. According to this procedure, this thesis thoroughly studies the techniques of built-up area detection, building detection and geometrical information extraction. The main work includes the following aspects.
(1) The phenomena of buildings imaged from a SAR sensor is analyzed and simulated. The main scattering mechanisms encountered in built-up areas, the typical imaging effects of buildings and their mapping relationship to a SAR image are analyzed. Then the characteristics of buildings in SAR images are concluded and validated by simulated images. All the analysis and simulation is the base of the subsequent research. Especially, simulation of building SAR images provides the testing data for the study of geometrical information extraction.
(2) According to the requirement of providing ROI for building detection, an effective and efficient method of detecting the built-up areas from SAR images using the variogram texture feature is proposed. First, the variogram texture feature can describe the strong dissimilarity of the built-up areas in SAR images, and thus discriminates the built-up and non-built-up areas. Second, according to the different variogram curve patterns of the built-up and non-built-up areas, the best texture lag for the binary classification can be easily determined. Third, the variogram texture feature can be computed efficiently. Based on the theoretical analysis of the redundant computation, a fast recursive algorithm for computing variogram is designed, which makes built-up areas detection more practical and can meet the application demands of providing ROI for building detection.
(3) Since the existing methods of building detection from SAR images are not robust for images with complex scene or different appearances of buildings, a method of building detection using the marker-controlled watershed transformation is proposed, aiming at detecting buildings with their whole and accurate boundaries from the built-up area. By introducing the marker-controlled watershed transformation, this method can make use of not only the characteristics of the building, which are strong scattering and high gray values, but also the characteristics of the surrounding background, which are the black netlike structures formed by roads and shadows. The combination of the characteristics of buildings and background can overcome the problems of linking neighboring buildings in complex scene or dividing a building into several parts when its gray values fluctuate greatly. Besides, the new method can get the closed boundaries of the buildings. Since the ROEWA edge detector, an edge detector for SAR images with good localization performance, is used, the detected building boundaries are also accurately localized. Furthermore, according to the typical shapes of the building in SAR images, a shape analysis method called direction-correlation analysis is proposed to remove the false alarms. The experiments invalidate that the new method is effective with high detection rate, low false-alarm rate and good localization performance. The detection results can be used in the process of extracting the buildings’geometrical information.
(4) Since the existing methods of extracting buildings’geometrical information have defects in accuracy or practicality, a new frame of geometrical information extraction is proposed via matching the geometrical model of a building and the real SAR image. On one hand, the frame maps the model into the image to determine the typical regions such as the layover and the shadow, which contain the geometrical information of a building. On the other hand, with the features such as the building boundary obtained from the building detection, the best model parameters can be found by matching the model and the detected features. The new frame is a general one, because the building model, the typical feature regions and the methods of detecting these regions can be adjusted in different applications. An implement of the frame is carried out using the common flat building as an example. First, the method of mapping a building model into the layover and the shadow regions with different imaging conditions are discussed. Second, the matching functions of the layover boundary and the shadow boundary are designed, and they form the whole matching function. Third, the algorithm of estimating the model parameters using the genetic algorithm is given. When the matching function reaches its maximum, the corresponding parameters are the best estimation for the building geometrical information. The experiments of both the simulated and the real images show the accuracy and practicality of the new frame: the use of the mapping relationship of the geometrical model to the image ensures the accuracy; the transform of extracting the geometrical information to maximizing the matching function and the automatic parameter estimation ensure its practicality.
(1)建筑物SAR图像的特征分析与仿真。分析了建筑区的主要散射机制和建筑物的典型成像效应及其到SAR图像上的映射关系,归纳总结了建筑物在SAR图像中的典型特征,并仿真建筑物的SAR图像对此进行了验证。这些工作为后续研究的开展奠定了基础,特别地,建筑物图像仿真还为建筑物几何信息提取阶段的研究提供了测试数据集。
(2)SAR图像中建筑区的检测。根据为建筑物检测阶段提供ROI区域的要求,以准确、高效地完成建筑区检测为目标,提出了一种基于变差函数纹理特征的SAR图像建筑区检测方法。该方法的特点与优势在于:首先,变差函数纹理特征能够刻画高分辨率SAR图像中建筑区所表现出来的很强的非相似性,易于区分图像中的建筑区与非建筑区;其次,通过分析建筑区与非建筑区变差函数曲线的规律,可以确定最适于建筑区-非建筑区两类分类的纹理间距;再次,变差函数纹理特征计算可高效完成,在理论分析其计算量的基础上,推导出了相应的快速算法,大大提高了建筑区检测方法的实用性,能够满足为后续建筑物检测提供ROI区域的应用需求。
(3)SAR图像中建筑物的检测。以完整、准确地检测到建筑区中单个建筑物目标轮廓为目标,针对现有检测方法对复杂场景以及不同建筑物图像的适用性难以保证的问题,提出一种基于标记控制分水岭变换的SAR图像建筑物检测方法。将标记控制的分水岭变换分割框架引入建筑物检测之中,可以同时利用建筑物自身的强散射、高灰度特点及其所处的黑色道路(阴影)网状结构的背景特点,二者的结合有效克服了复杂场景中相邻建筑物目标易于错误连接、而单个建筑物由于自身灰度分布不均又易于断裂成多个部分的问题。新方法可得到建筑物目标的闭合轮廓,由于采用了适于SAR图像且定位性能良好的ROEWA边缘检测器,使分割得到的建筑物轮廓也具有定位准确的特点。此外,根据SAR图像中建筑物的典型形状特征,提出一种方向相关的形状分析法,可进一步有效地排除虚警。实验结果证明新方法具有检测率高、虚警率低、定位准确等优点,是一种有效的方法,所得结果能对后续建筑物几何信息的提取提供有力支持。
(4)SAR图像中建筑物几何信息的提取。针对现有方法存在精度不高或实用性太低的问题,提出了一种基于模型与图像匹配度的建筑物几何信息提取框架。该框架一方面将建筑物模型映射到图像上,确定图像中包含有建筑物几何信息的典型特征区域如叠掩和阴影区域,另一方面利用建筑物检测阶段得到的建筑物目标轮廓等信息,通过二者的匹配度来寻求最佳的模型参数。新框架具有较强的通用性,建筑物参数化几何模型、典型特征区域及其提取方法都可以随实际应用而调整。以最常见的平顶长条形建筑物为例,讨论了该框架的一个具体应用:详细分析了在不同成像参数与模型参数条件下,将建筑物模型映射为图像中叠掩和阴影典型特征区域的方法;设计了叠掩边界匹配度和阴影边界匹配度来衡量模型与图像的匹配程度;给出了基于遗传算法的最优化求解方法,取匹配度函数最大化时的模型参数作为待求解建筑物的几何参数。仿真和实测数据实验结果表明,由于新方法引入了由建筑物几何模型到图像的精确映射关系,可确保求解的精度;而且将几何信息提取问题转化为匹配度函数最大化问题,可利用优化算法自动求解,无需人工操作,大大提高了实用性。
With the demand on urban SAR image interpretation, techniques of building extraction from high-resolution SAR images are investigated in this thesis. Aiming at dealing with high-resolution and large-scene images in practical applications, a hierarchical procedure for building extraction is proposed. First, the built-up areas are detected from the large-scene image; then, building detection is carried out in the detected built-up areas; finally, the geometrical information of the buildings is extracted. According to this procedure, this thesis thoroughly studies the techniques of built-up area detection, building detection and geometrical information extraction. The main work includes the following aspects.
(1) The phenomena of buildings imaged from a SAR sensor is analyzed and simulated. The main scattering mechanisms encountered in built-up areas, the typical imaging effects of buildings and their mapping relationship to a SAR image are analyzed. Then the characteristics of buildings in SAR images are concluded and validated by simulated images. All the analysis and simulation is the base of the subsequent research. Especially, simulation of building SAR images provides the testing data for the study of geometrical information extraction.
(2) According to the requirement of providing ROI for building detection, an effective and efficient method of detecting the built-up areas from SAR images using the variogram texture feature is proposed. First, the variogram texture feature can describe the strong dissimilarity of the built-up areas in SAR images, and thus discriminates the built-up and non-built-up areas. Second, according to the different variogram curve patterns of the built-up and non-built-up areas, the best texture lag for the binary classification can be easily determined. Third, the variogram texture feature can be computed efficiently. Based on the theoretical analysis of the redundant computation, a fast recursive algorithm for computing variogram is designed, which makes built-up areas detection more practical and can meet the application demands of providing ROI for building detection.
(3) Since the existing methods of building detection from SAR images are not robust for images with complex scene or different appearances of buildings, a method of building detection using the marker-controlled watershed transformation is proposed, aiming at detecting buildings with their whole and accurate boundaries from the built-up area. By introducing the marker-controlled watershed transformation, this method can make use of not only the characteristics of the building, which are strong scattering and high gray values, but also the characteristics of the surrounding background, which are the black netlike structures formed by roads and shadows. The combination of the characteristics of buildings and background can overcome the problems of linking neighboring buildings in complex scene or dividing a building into several parts when its gray values fluctuate greatly. Besides, the new method can get the closed boundaries of the buildings. Since the ROEWA edge detector, an edge detector for SAR images with good localization performance, is used, the detected building boundaries are also accurately localized. Furthermore, according to the typical shapes of the building in SAR images, a shape analysis method called direction-correlation analysis is proposed to remove the false alarms. The experiments invalidate that the new method is effective with high detection rate, low false-alarm rate and good localization performance. The detection results can be used in the process of extracting the buildings’geometrical information.
(4) Since the existing methods of extracting buildings’geometrical information have defects in accuracy or practicality, a new frame of geometrical information extraction is proposed via matching the geometrical model of a building and the real SAR image. On one hand, the frame maps the model into the image to determine the typical regions such as the layover and the shadow, which contain the geometrical information of a building. On the other hand, with the features such as the building boundary obtained from the building detection, the best model parameters can be found by matching the model and the detected features. The new frame is a general one, because the building model, the typical feature regions and the methods of detecting these regions can be adjusted in different applications. An implement of the frame is carried out using the common flat building as an example. First, the method of mapping a building model into the layover and the shadow regions with different imaging conditions are discussed. Second, the matching functions of the layover boundary and the shadow boundary are designed, and they form the whole matching function. Third, the algorithm of estimating the model parameters using the genetic algorithm is given. When the matching function reaches its maximum, the corresponding parameters are the best estimation for the building geometrical information. The experiments of both the simulated and the real images show the accuracy and practicality of the new frame: the use of the mapping relationship of the geometrical model to the image ensures the accuracy; the transform of extracting the geometrical information to maximizing the matching function and the automatic parameter estimation ensure its practicality.
引文
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