刘少创, 林宗坚. 基于可变模板的航空影像中建筑物提取[J]. 武汉大学学报 ( 信息科学版), 1997, 22(1): 21-28.
引用本文: 刘少创, 林宗坚. 基于可变模板的航空影像中建筑物提取[J]. 武汉大学学报 ( 信息科学版), 1997, 22(1): 21-28.
Liu Shaochuang, Lin Zongjian. Deformable-Template-Based Buildings Detection from Aerial Imagery[J]. Geomatics and Information Science of Wuhan University, 1997, 22(1): 21-28.
Citation: Liu Shaochuang, Lin Zongjian. Deformable-Template-Based Buildings Detection from Aerial Imagery[J]. Geomatics and Information Science of Wuhan University, 1997, 22(1): 21-28.

基于可变模板的航空影像中建筑物提取

Deformable-Template-Based Buildings Detection from Aerial Imagery

  • 摘要: 针对航空影像中目标提取的困难,提出了一种基于可变模板的航空影像中建筑物提取方法,并针对几种比较典型的建筑物,详细讨论了模板的设计、目标初始参数的获取及模板最优参数求解等几个关键性的问题。采用这种方法进行航空影像中目标的提取时,可以将影像中的多种信息和人的识别能力进行有效的融合,因而能够达到可靠地提取影像中目标的目的。作为一种有效的优化求解方法,模拟退火被用于目标提取的优化过程。为了验证这种方法提取航空影像中目标的能力,给出了利用可变模板提取航空影像中几种不同类型的建筑物的例子。

     

    Abstract: Buildings detection is an important research in the area of computer vision and digital photogrammetry.A new approach of buildings detection based on deformable templates is presented in this paper.This method is based on the synergy of different information to address the problem of buildings detection from a complex aerial image.The problem of building localization and identification is treated as a process of matching a deformable template to the building boundary in the aerial image that inputs to the system.The features of the buildings such as image intensity,image gradient and geometric constraints etc.are described by energy terms.The weighted sum of each energy term is the total energy of the deformable template.Prior knowledge of a building shape is described by a parameter vector that consists of a set of parameters.The prior geometric information is obtained from seeds.The operator of the system specifies the seeds by activating the mouse.The image intensity information and image gradient information are obtained from the input aerial image by pre processing.Simulated annealing is used in the optimizing process.The optimizing parameter vector can give the geometric and semantic information of the target in image experimental results are presented to demonstrate the efficiency of this approach.

     

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