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Building outline vectorization from high spatial resolution imagery

  • role： First author第一作者
  • Affiliation：

    Anhui & Huaihe River Institute of Hydraulic Research, Hefei 230088, China

    Anhui Dayu Project Construction Supervision and Consult Co., Ltd., Hefei 230088, China

  • Email：sun_jin_yan@126.com
  • Introduction：孙金彦(1988—　)，女，硕士研究生，研究方向为水环境遥感、图像解译和机器学习。E-mail：sun_jin_yan@126.com
SUN Jinyan12,  
  • Affiliation：

    Anhui & Huaihe River Institute of Hydraulic Research, Hefei 230088, China

    Anhui Dayu Project Construction Supervision and Consult Co., Ltd., Hefei 230088, China

HUANG Zuoji12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Anhui & Huaihe River Institute of Hydraulic Research, Hefei 230088, China

  • Email：zhousg1966@126.com
  • Introduction：周绍光(1966—　)，男，副教授，研究方向为摄影测量与遥感和数字图分析。E-mail：zhousg1966@126.com
ZHOU Shaoguang4*,  
  • Affiliation：

    Anhui & Huaihe River Institute of Hydraulic Research, Hefei 230088, China

XU Nan3,  
  • Affiliation：

    Anhui & Huaihe River Institute of Hydraulic Research, Hefei 230088, China

    Anhui Dayu Project Construction Supervision and Consult Co., Ltd., Hefei 230088, China

QIAN Haiming12,  
  • Affiliation：

    Anhui & Huaihe River Institute of Hydraulic Research, Hefei 230088, China

    Anhui Dayu Project Construction Supervision and Consult Co., Ltd., Hefei 230088, China

WANG Chunlin12

resumen

The extraction of building outlines from high spatial resolution imagery is a key element of numerous geospatial applications and has been addressed by various approaches. However, the final extraction results are always irregular or inaccurate owing to the boundary regularization algorithm and variability of building shape. This paper proposes a new method for the regularization and vectorization of two dimensional building outlines from high spatial resolution imagery. To accomplish this task, we utilize image segmentation to detect the two main orientations (θ and θ+90°) of the building blobs. To effectively refine boundaries, we then divide the boundary points, which were obtained clockwise, into the first principal, second principal, and unknown orientation classes. Then, we use least square template matching to precisely position the edge to reduce accuracy loss. Finally, the building outlines are generated by connecting the corner points with intersecting adjacent lines. In addition to the omission buildings, experimental results confirm the ability of the presented system to effectively and steadily extract building outlines with an overall average correctness of 89%, completeness of 98%, shape accuracy of 87%, and quality of 85%. This method can be widely used in various applications. Specifically, our method can work with a relatively low-accuracy image segmentation. Therefore, it can be applied for the vector quantization of large-area building outlines. However, our method only focuses on building outlines and does not consider the internal structure of the building. In the future, more attention should be givento solve this issue.

palabra clave

remote sensing image;building vectorization;building outline;α-expansion;template matching;regularization

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