HF-FCN: Hierarchically Fused Fully Convolutional Network for Robust Building Extraction

  • Tongchun Zuo
  • Juntao Feng
  • Xuejin ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)


Automatic building extraction from remote sensing images plays an important role in a diverse range of applications. However, it is significantly challenging to extract arbitrary-size buildings with largely variant appearances or occlusions. In this paper, we propose a robust system employing a novel hierarchically fused fully convolutional network (HF-FCN), which effectively integrates the information generated from a group of neurons with multi-scale receptive fields. Our architecture takes an aerial image as the input without warping or cropping it and directly generates the building map. The experiment results tested on a public aerial imagery dataset demonstrate that our method surpasses state-of-the-art methods in the building detection accuracy and significantly reduces the time cost.


Local Binary Pattern Conditional Random Field Aerial Image Variant Appearance Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (NSFC) under Nos. 61472377 and 61331017, and the Fundamental Research Funds for the Central Universities under No. WK2100060011.


  1. 1.
    Noronha, S., Nevatia, R.: Detection and modeling of buildings from multiple aerial images. IEEE Trans. Pattern Anal. Mach. Intell. 23, 501–518 (2001)CrossRefGoogle Scholar
  2. 2.
    Nosrati, M.S., Saeedi, P.: A novel approach for polygonal rooftop detection in satellite/aerial imageries. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 1709–1712 (2009)Google Scholar
  3. 3.
    Izadi, M., Saeedi, P.: Three-dimensional polygonal building model estimation from single satellite images. IEEE Trans. Geosci. Remote Sens. 50, 2254–2272 (2012)CrossRefGoogle Scholar
  4. 4.
    Wang, J., Yang, X., Qin, X., Ye, X., Qin, Q.: An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery. IEEE Geosci. Remote Sens. Lett. 12, 487–491 (2015)CrossRefGoogle Scholar
  5. 5.
    Cote, M., Saeedi, P.: Automatic rooftop extraction in nadir aerial imagery of suburban regions using corners and variational level set evolution. IEEE Trans. Geosci. Remote Sens. 51, 313–328 (2013)CrossRefGoogle Scholar
  6. 6.
    Sirmacek, B., Unsalan, C.: Building detection from aerial images using invariant color features and shadow information. In: 23rd International Symposium on Computer and Information Sciences, ISCIS 2008, pp. 1–5 (2008)Google Scholar
  7. 7.
    Manno-Kovcs, A., Ok, A.O.: Building detection from monocular VHR images by integrated urban area knowledge. IEEE Geosci. Remote Sens. Lett. 12, 2140–2144 (2015)CrossRefGoogle Scholar
  8. 8.
    Chen, D., Shang, S., Wu, C.: Shadow-based building detection and segmentation in high-resolution remote sensing image. J. Multimedia 9, 181–188 (2014)Google Scholar
  9. 9.
    Ngo, T.T., Collet, C., Mazet, V.: Automatic rectangular building detection from VHR aerial imagery using shadow and image segmentation. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1483–1487 (2015)Google Scholar
  10. 10.
    Baluyan, H., Joshi, B., Al Hinai, A., Woon, W.L.: Novel approach for rooftop detection using support vector machine. ISRN Mach. Vis. 2013 (2013)Google Scholar
  11. 11.
    Li, E., Femiani, J., Xu, S., Zhang, X., Wonka, P.: Robust rooftop extraction from visible band images using higher order CRF. IEEE Trans. Geosci. Remote Sens. 53, 4483–4495 (2015)CrossRefGoogle Scholar
  12. 12.
    Mnih, V.: Machine learning for aerial image labeling. Doctoral (2013)Google Scholar
  13. 13.
    Saito, S., Yamashita, Y., Aoki, Y.: Multiple object extraction from aerial imagery with convolutional neural networks. J. Imaging Sci. Technol. 60 (2016)Google Scholar
  14. 14.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)Google Scholar
  15. 15.
    Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.S.: Conditional random fields as recurrent neural networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1529–1537 (2015)Google Scholar
  16. 16.
    Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1520–1528 (2015)Google Scholar
  17. 17.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results.
  18. 18.
    Ghaffarian, S., Ghaffarian, S.: Automatic building detection based on purposive FastICA (PFICA) algorithm using monocular high resolution google earth images. ISPRS J. Photogramm. Remote Sens. 97, 152–159 (2014)CrossRefGoogle Scholar
  19. 19.
    Dornaika, F., Moujahid, A., Bosaghzadeh, A., El Merabet, Y., Ruichek, Y.: Object classification using hybrid holistic descriptors: application to building detection in aerial orthophotos. Polibits 51, 11–17 (2015)CrossRefGoogle Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2015)Google Scholar
  21. 21.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. Eprint Arxiv, pp. 675–678 (2014)Google Scholar
  22. 22.
    Xie, S., Tu, Z.: Holistically-nested edge detection. In: The IEEE International Conference on Computer Vision (ICCV) (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application SystemUniversity of Science and Technology of ChinaHefeiChina

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