предыдущий|следующий

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

реферат

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.

ключеви́че слова́

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

References

  1. 1.
    Boykov Y, Veksler O and Zabih R. 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11): 1222–1239
  2. 2.
    Boykov Y and Kolmogorov V. 2004. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9): 1124–1137
  3. 3.
    Chaudhuri D and Samal A. 2007. A simple method for fitting of bounding rectangle to closed regions. Pattern Recognition, 40(7): 1981–1989
  4. 4.
    Cote M and Saeedi P. 2013. Automatic rooftop extraction in nadir aerial imagery of suburban regions using corners and variational level set evolution. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 313–328
  5. 5.
    Dutter M. 2007. Generalization of building footprints derived from high resolution remote sensing data. Pennsylvania State:The Pennsylvania State University CiteSeerX Archives
  6. 6.
    Gruen A and Agouris P. 1994. Linear feature extraction by least squares template matching constrained by internal shape forces//Proceedings of SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision. Munich, Federal Republic of Germany: SPIE:316[DOI: 10.1117/12.182860]
  7. 7.
    Hu X Y, Zhang Z X and Li J. 2009. Linear feature extraction using adaptive least-squares template matching and a scalable slope edge model. International Journal of Remote Sensing, 30(13): 3393–3407
  8. 8.
    Li Z B, Liu Z Z and Shi W Z. 2014. A fast level set algorithm for building roof recognition from high spatial resolution panchromatic images. IEEE Geoscience and Remote Sensing Letters, 11(4): 743–747
  9. 9.
    Lu J W. 2006. A Study of Building Extraction from High Resolution Remote Sensing Images.Beijing: University of Chinese Academy of Sciences
  10. 10.
    Maas H G and Vosselman G. 1999. Two algorithms for extracting building models from raw laser altimetry data. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2/3): 153–163
  11. 11.
    Meng Y B. 2007. Study on the building contour extraction from high resolution satellite image. Fuxin: Liaoning Technical University
  12. 12.
    OkAO. 2013. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS Journal of Photogrammetry and Remote Sensing, 86:21–40
  13. 13.
    Rau J Y and Chen L C. 2003. Fast straight lines detection using Hough transform with principal axis analysis. Journal of Photogrammetry and Remote Sensing, 8(1): 15–34
  14. 14.
    Sampath A and Shan J. 2007. Building boundary tracing and regularization from airborne lidar point clouds. Photogrammetric Engineering and Remote Sensing, 73(7): 805–812
  15. 15.
    Shen W, Li J, Chen Y H, Deng L and Peng G X. 2008. Algorithms study of building boundary extraction and normalization based on lidar data. Journal of Remote Sensing, 12(5): 692–698
  16. 16.
    Wu X Y. 2011. Research on Extraction and Contour Vectorizationof Buildings from High-Resolution Remote Sensed Images. Nanjing: Nanjing University
  17. 17.
    Yang H C, Deng K Z and Zhang S B. 2006. Semi-automated extraction from aerial image using improved Hough transformation. Science of Surveying and Mapping, 31(6): 93–94, 97
  18. 18.
    Zeng C Q, Wang J F and Lehrbass B. 2013. An evaluation system for building footprint extraction from remotely sensed data. IEEE Journal ofSelected Topics in Applied Earth Observations and Remote Sensing, 6(3): 1640–1652
  19. 19.
    Zhang K, Yan J and Chen S C. 2006. Automatic construction of building footprints from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2523–2533
  20. 20.
    Zhou S G, Sun J Y, Fan L, Xiang J and Chen C. 2015. Extraction of building contour from high resolution images. Remote Sensing for Land and Resources, 27(3): 52–58

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website