CASIA OpenIR  > 毕业生  > 博士学位论文
新型高阶主动轮廓模型、形状先验和多尺度分析:超高分辨率卫星图像中的道路网络提取应用研究
其他题名New Higher-Order Active Contour Models, Shape Priors, and Multiscale Analysis: Their Application to Road Network Extraction from VHR Satellite Images
彭婷
学位类型工学博士
导师胡包钢 ; 普林特
2008-11-18
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
关键词高阶主动轮廓 相位场 先验形状 多分辨率 参数 道路网络提取 地图更新 高分辨率 密集城市区域 遥感图像 Higher-order Active Contour (Hoac) Phase Field Prior Shape Multiresolution Parameter Road Network Extraction Map Updating Very High Resolution (Vhr) Dense Urban Area Remote Sensing Images
摘要从一幅图像中分割出具有“网络”形式(即相交在交点的分支)的对象,这一问题来自多个领域的应用要求。例如,遥感图像中道路和河流网络的分割,以及医学图像中血管网络的分割。从遥感图像中检测道路对很多应用都至关重要。近些年,随着小于米级别的商业超高分辨率(very high resolution,VHR)图像的出现,为遥感图像的信息提取带来新的契机。但也带来很多困难,这导致已有的道路提取算法的失效。 本论文的目标是开发和验证鲁棒的方法,从密集城区的VHR光学卫星图像中半自动地提取道路网络。VHR图像本身很复杂,而且网络区域可能有任意的拓扑,所以这是一个难题。我们的模型构造于最新提出的高阶主动轮廓(higher-order active contour,HOAC)相位场框架的基础上。与文献中的工作相比,我们做出以下两点主要贡献。从应用的角度看,向着VHR光学卫星图像的自动道路提取系统的实现迈进一大步。从模型的角度看,构造新的用于网络建模的HOAC能量,引入不同类型的形状先验,并对模型进行多分辨率分析。 本论文首先简要地介绍我们要处理的问题,然后给出在主动轮廓和道路提取领域的综述。接下来的章节详细地介绍我们的方法、实现及结果: 1. 为了处理VHR图像中存在信息的复杂性,第四章提出多分辨率统计数据模型,以及多分辨率约束的先验模型。它们都可以综合从粗略分辨率到精确分辨率的分割结果。 2. 第五章,在道路地图更新的特例中,提出特定先验模型,它是从过时的地理信息系统(GIS)数字地图中导出得到的。HOAC模型描述道路网络通用的几何形状,而这一特定先验项可以平衡这一通用先验知识的作用。 3. 然而,传统的HOAC模型存在一个严重局限:网络分支宽度被约束为网络分支的最大曲率半径,从而对直且窄的分支,或者高度弯曲且宽的分支不能有效建模。为了解决这个问题,第六章引入两个新的模型:一个带有额外的非线性非局部HOAC项,而另一个带有额外的线性非局部HOAC项。两者都可对分支宽度和分支曲率实现分离控制,从而在相同宽度的条件下提供更好的延伸性。但是,线性项有着更多的优点。从计算角度来看,它效率更高;而且它能够同时对多个宽度建模。为了解决这些模型的参数选择这一难题,对一个给定宽度的长杆,我们分析这些能量的稳定性条件,并说明如何严格地选择能量函数的参数。 在VHR快鸟(~0.61m/像素)卫星图像上的实验,以及与其它算法的比较,显示出我们模型的优越性。
其他摘要The need to segment, from an image, entities that have the form of a ‘network’, i.e. branches joining together at junctions, arises in a variety of domains. Examples include the segmentation of road and river networks in remote sensing imagery, and of vascular networks in medical imagery. Detecting roads from remotely sensed imagery is critical for many applications. Recently, the commercial availability of VHR images, with sub-metric resolution, provides new opportunities for the extraction of information from remotely sensed imagery. However, it brings many difficulties as well, which results in the relative failure of the existing road extraction algorithms. The objective of this thesis is to develop and validate robust approaches for the semi-automatic extraction of road networks in dense urban areas from VHR optical satellite images. The task is difficult for two main reasons: VHR images are intrinsically complex and network regions may have arbitrary topology. Our models are based on the recently developed HOAC phase field framework. We make two main contributions. In problem-specific terms, we make progress towards an automatic road extraction system for VHR optical satellite images. In methodological terms, we construct novel HOAC energies for network modeling, introduce different types of shape priors, and conduct the multiresolution analysis of the model. In this thesis, after a brief introduction to the problem, we present the state-of-the-art for active contours and road extraction. The following chapters then present in details our methodology, implementation and results: 1. In chapter 4, to tackle the complexity of the information contained in VHR images, we propose a multiresolution statistical data model and a multiresolution constrained prior model. They enable the integration of segmentation results from coarse resolution to fine resolution. 2. In chapter 5, for the particular case of road map updating, we present a specific prior model derived from an outdated GIS digital map. This specific prior term balances the effect of the generic prior knowledge carried by the HOAC model, which describes the geometric shape of road networks in general. 3. However, the classical HOAC model suffers from a severe limitation: network branch width is constrained to be similar to maximum network branch radius of curvature. In chapter 6, we solve this problem by introducing two new models: one with an additional nonlinear nonlocal HOAC term, and one with an additional linear nonlocal HOAC term. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient from a computational standpoint, and it is able to model multiple widths simultaneously. Experiments on VHR QuickBird satellite images and comparisons with other approaches demonstrate the superiority of our models.
馆藏号XWLW1292
其他标识符200518014628040
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6126
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
彭婷. 新型高阶主动轮廓模型、形状先验和多尺度分析:超高分辨率卫星图像中的道路网络提取应用研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
CASIA_20051801462804(23416KB) 暂不开放CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[彭婷]的文章
百度学术
百度学术中相似的文章
[彭婷]的文章
必应学术
必应学术中相似的文章
[彭婷]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。