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高分辨率卫星图像中密集城区建筑物自动提取算法研究
其他题名Research on Building Extraction from Densely
宋宗莹
学位类型工学博士
导师杨青
2008-05-21
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词高分辨率卫星图像 城市 建筑物 提取 区域 边验证 假设-验证 规则度 纹理 High Resolution Satellite Image Urban Building Extraction Region Analysis Hypothesis-verification Edge Verification Regularity Texture
摘要近年来,随着几颗高分辨率商用遥感卫星的成功发射,如Ikonos、QuickBird等,高分辨率遥感图像资源越来越丰富。基于这些图像,人类几乎能够实时、多角度、高清晰的观测人类赖以生存的地球,因此高分辨率遥感图像在许多应用领域引起了广泛的关注,如地理信息系统(GIS)制作与更新、地图制作、城区规划、抗灾救灾、环境监测和数字地球数字城市等领域。然而与图像质量高速提升相对应的技术和理论发展却相对滞后,特别是城市地物提取技术,已经严重阻碍了空间信息采集技术自动化和智能化的发展进程。因此,本文我们针对具有实际应用背景的密集城区高分辨率卫星图像,展开城区地物主要构成要素-建筑物的自动提取技术的研究。本文的工作和贡献主要有以下几个方面: 1.提出了一种基于区域的2D建筑物自动提取算法。算法首先利用SVM和纹理特征对图像进行分类,最优的识别出我们感兴趣的建筑物类区域,然后提出了一个基于种子点的区域合并算法求取建筑物的覆盖区域并以此产生建筑物屋顶的轮廓假设,接下来利用线段特征对错误边缘进行纠正,最后利用阴影和几何约束剔除非建筑物假设。实验结果也展示了该算法的性能和有效性。 2.提出了一种基于边验证的2D建筑物自动提取算法。}该算法以上一章求出的建筑物覆盖区域为基础,提出了一个新的假设-验证框架,算法的核心为边验证算法。由于高分辨率图像中产生建筑物假设的整体性能比较接近,因此我们不利用总体性能进行假设优劣判别,而是将整体假设的验证分解成为建筑物各个不同方向的边的验证,利用假设集合中边与边之间的相互关联关系进行相互验证。算法的基本思路是:对建筑物各个不同方向的所有备选边赋予一个概率,该概率用来表示备选边成为该方向上最优边的可能性,然后利用图像特征估计任意两个备选边概率之间的约束关系,最后基于这些约束关系,构建了一个二次规划的优化问题,通过求解这个最优化问题使每条备选边的概率最大程度的和利用图像特征所估计出来的边的概率之间的约束关系相一致,从而能够对这些概率进行最优估计。实验结果证明该算法能够非常有效的从复杂场景中辨别出建筑物的正确边缘。 3.提出了一种评价物体边缘规则度的方法和构建了一个建筑物内部区域一致性的图像特征评价体系。边缘规则度度量方法针对离散图像中物体边缘的分布特点,定义4个规则边缘描述子,然后利用统计的方法对物体边缘的规则度进行度量。为了有效的描述建筑物内部区域的一致性,我们从建筑物内部、周围环境和阴影三个方面,构建了完整的图像特征评价体系,为建筑物提取提供全面的可靠的图像特征。
其他摘要Recently, the successful launch of several commercial optical satellites, such as Ikonos and QuickBird, has greatly enriched the resources of high resolution satellite images, which enables the high clarity, multi-angle and even real time observation of the earth. Consequently, the high resolution satellite images are drawning more and more attenstion from multiple fields, such as the creation and update of GIS data, the cartography, urban planning, disaster monitoring, environment monitoring and digital earch, etc. However, the current development of the corresponding theories and techniques, especially in the field of object extraction from urban images, is too slow to handle the increasingly improved data quality, which has obviously weakened the automation and intelligence of the spacial information collection. Hence, the main work of our thesis is focused on the building extraction from densely built-up urban images, and the major achievements and contributions are summarized as following: 1.Propose a 2D building extraction algorithm based on region analysis.In our algorithm, we first recognize the building regions by SVM and texture features, and then we propose a seed-based region grouping method to estimate the location and cover areas of all buildings, after that, building hypotheses are generated based on these estimated cover areas, and lines are used to refine some error edge locations, finally the shadow and geometry constraints are used to delete the noises. 2.Propose a 2D building extraction algorithm based on edge verification. The whole algorithm follows the hypothesis-verification strategy. The key contribution of this approach is the edge verification method during the hypothesis verification process, which greatly improves the accuracy of the building extraction results from complex scenes. To extract more accurate edges, we construct a probabilistic model and an optimization framework: first, a probability being the optimal edge is given to each possible edge, then the constraints of any two possible edges are estimated based on a machine learning method incorporating the image evidences, finally, these constraints and other prior knowledge are integrated into an optimization problem, by solving which these probabilities can be computed and the optimal edge can be selected. 3.Propose a method to evaluate the regularity of man-made object contours and construct an evaluation system for the consistency of different parts of buildings.The regularity evaluation method constructs four discrete regular edge descriptors, based on which a statistic method is used to describe the regularity of contours. To analyze the consistency of different parts of buildings, we construct an image feature system from the interior, the outer supporting regions and the shadows of target building. This is a completed and reliable building extraction feature model, which is very helpful for objects extraction from complex scenes.
馆藏号XWLW1259
其他标识符200518014628092
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6060
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
宋宗莹. 高分辨率卫星图像中密集城区建筑物自动提取算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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