Building extraction from high resolution remotely sensed imagery based on shadows and graph-cut segmentation

被引：0

作者：

Shi W.-Z. ^{[1
,2
,3
,4
]}

Mao Z.-Y. ^{[1
,3
,4
]}

机构：

[1] Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350002, Fujian

[2] College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350108, Fujian

[3] National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, Fujian

[4] Spatial Information Engineering Research Centre of Fujian Province, Fuzhou University, Fuzhou, 350002, Fujian

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2016年 / 44卷 / 12期

关键词：

Building extraction; Graph-cut; Remotely sensed imagery; Shadows;

D O I：

10.3969/j.issn.0372-2112.2016.12.006

中图分类号：

学科分类号：

摘要：

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 precise 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 respectively, while buildings and their exact boundaries were extracted with adjacency between processed shadows and candidate segmentation objects. For verifying the validity of the proposed method, six sub-images were chosen from PLEIADES images. Experimental results show that the average precision and recall of the proposed method are 92.31% and 74.23% respectively. © 2016, Chinese Institute of Electronics. All right reserved.

引用

页码：2849 / 2854

页数：5

共 13 条

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