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Building Segmentation of Aerial Images in Urban Areas with Deep Convolutional Neural Networks

  • Yaning Yi
  • Zhijie Zhang
  • Wanchang Zhang
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Building segmentation of aerial images in urban areas is of great importance for many applications, such as navigation, change detection, areal monitoring and urban planning. However, due to the uncertainties involved in images, a detailed and effective solution is still critical for further applications. In this paper, we proposed a novel deep convolutional neural network for building segmentation of aerial images in urban areas, which was based on the down-sampling-then-up-sampling architecture. The suggested network is similar to that of the FCN, but with ours differs as it takes into account the multi-scale features using Atrous Spatial Pyramid Pooling. Additionally, motivated by the recent published works, the depth-wise separable convolution was also adopted to replace the standard convolution in our proposed method, which largely reduced the training parameters. To evaluate the performance of our proposed method, a very high resolution aerial image dataset (0.075 m) was used to train and test the images. In addition, two state-of-the-art methods named FCN-8s and SegNet were also compared with our method for performance evaluations. The experiments demonstrated that our method outperformed the state-of-the-art methods greatly both in terms of qualitative and quantitative performance.

Keywords

Convolutional neural networks Building segmentation Aerial image Depth-wise separable convolution Atrous convolution 

Notes

Acknowledgements

This study was financially supported by the National Key Research and Development Program of China (Grant No. 2016YFB0502502 and No. 2016YFA0602302).

References

  1. 1.
    Volpi, M., Tuia, D.: Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55(2), 881–893 (2017)CrossRefGoogle Scholar
  2. 2.
    Miao, Z., Fu, K., Sun, H., Sun, X., Yan, M.: Automatic water-body segmentation from high-resolution satellite images via deep networks. IEEE Geosci. Remote Sens. Lett. (99), 1–5 (2018)Google Scholar
  3. 3.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. IEEE, Boston, MA, USA (2015)Google Scholar
  4. 4.
    Li, J., Ding, W., Li, H., Liu, C.: Semantic segmentation for high-resolution aerial imagery using multi-skip network and Markov random fields. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 12–17. IEEE, Beijing, China (2017)Google Scholar
  5. 5.
    Marmanis, D., Wegner, J.D., Galliani, S., Schindler, K., Datcu, M., Stilla, U.: Semantic segmentation of aerial images with an ensemble of CNNS. ISPRS Annal. Photogrammetry Remote Sens. Spat. Inf. Sci. 3(3), 473–480 (2016)CrossRefGoogle Scholar
  6. 6.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807. IEEE, Honolulu, HI, USA (2017)Google Scholar
  7. 7.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Key Laboratory of Digital Earth ScienceInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Center for Environmental Science and EngineeringUniversity of ConnecticutStorrsUSA

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