摘要
高空间分辨率遥感影像的建筑物自动提取能够加速城市基础地理数据库的更新进程.建筑物提取方法存在的一个亟需解决的问题是建筑物轮廓难以准确提取.本文通过建筑物的阴影特征和图割提出一种在高分辨率遥感影像中识别与提取建筑物的方法.首先,基于势直方图函数检测阴影;然后,以长宽比和矩形度作为约束条件从图割结果中筛选出候选分割对象;最后,利用开运算、膨胀和腐蚀分别对阴影进行处理,计算处理后的阴影和候选分割对象之间的邻接关系得到建筑物及其轮廓.为了验证本文方法的有效性,选取PLEIADES影像中6幅具有代表性的子图像进行试验,结果表明本方法的平均查准率和平均查全率分别达到92.31%和74.23%.
Automatic building extraction from high spatial resolution remotely sensed imagery can accelerate the update process for urban basic geographic database. One problem of building extraction methods is the difficulty of extracting the pre- cise building contour. This article proposes an approach to recognizing and extracting buildings from high resolution remotely sensed imagery based on shadows and graph-cut segmentation. Firstly, shadows were detected by using potential histogram function. Then,candidate segmentation objects were selected from the result of graph-cut segmentation with the constraint by integrating aspect ratio and rectangularity. At last, shadows were processed with open, dilate and corrode operations respective- ly, while buildings and their exact boundaries were extracted with adjacency between processed shadows and candidate segmen- tation objects. For verifying the validity of the proposed method, six sub-images were chosen from PLEIADES images. Experi- mental results show that the average precision and recall of the proposed method are 92. 31% and 74. 23% respectively.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2016年第12期2849-2854,共6页
Acta Electronica Sinica
基金
"十二五"国家科技支撑计划项目(No.2013BAC08B02-01)
国家重点基础研究发展计划项目课题(No.2006CB708306)
福建省教育厅项目(No.JB14038)
关键词
遥感影像
阴影
图割
建筑物提取
remotely sensed imagery
shadows
graph-cut
building extraction
