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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
Acknowledgements
This study was financially supported by the National Key Research and Development Program of China (Grant No. 2016YFB0502502 and No. 2016YFA0602302).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yi, Y., Zhang, Z., Zhang, W. (2019). Building Segmentation of Aerial Images in Urban Areas with Deep Convolutional Neural Networks. In: El-Askary, H., Lee, S., Heggy, E., Pradhan, B. (eds) Advances in Remote Sensing and Geo Informatics Applications. CAJG 2018. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-01440-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-01440-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01439-1
Online ISBN: 978-3-030-01440-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)