利用贝叶斯结构模型的复杂形状建筑物提取

Complex Shape Building Extraction with Bayesian Structure Model

现有的建筑物轮廓提取方法在处理非矩形的复杂形状建筑物时存在局限性,为克服这一缺点,提出一种航拍图像复杂形状建筑物轮廓的提取方法.针对复杂形状建筑物结构不规则的特点,提出一种贝叶斯结构模型.该模型以概率的形式表示建筑物边缘之间的连接关系;并引入Boosting决策树方法,综合考虑建筑物边缘周围的颜色和纹理等多种图像信息,增强了模型的鲁棒性.另外,设计了一种长度归一化的能量函数来描述建筑物轮廓,并运用图优化算法全局寻优,实现对建筑物轮廓的提取.实验结果表明,该方法能有效地提取多种复杂形状建筑物的轮廓.

The existing methods for building boundary extraction have limitations in handling non- rectangular complex shape buildings. To address this problem, a method to extract boundaries of complex shape buildings from an aerial image is presented. To account for the irregular structures of complex shape buildings, a Bayesian structure model （BSM） is proposed to represent the relationships of building edges in a probabilistic manner. The Boosting decision tree algorithm is used to fuse multiple types of image features such as color and texture around the building edges, which enhances the robustness of the model. Moreover, a boundary length normalized energy function is designed to describe the building boundary. The boundary extraction is implemented by a global optimization of the function on a graph. The experimental results demonstrate that the proposed method can effectively extract the boundaries of a variety of complex shape buildings.