Feature transfer based adversarial domain adaptation method for cross-domain road extraction

References

• Alshehhi R, Marpu PR, Woon WL, Mura MD. 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens. 130:139–149.
   Web of Science ®Google Scholar
• Badrinarayanan V, Kendall A, Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans Pattern Anal Mach Intell. 39(12):2481–2495.
   PubMed Web of Science ®Google Scholar
• Chandra N, Ghosh JK, Sharma A. 2019. A cognitive framework for road detection from high-resolution satellite images. Geocarto Int. 34(8):909–924.
   Web of Science ®Google Scholar
• Clevert D-A, Unterthiner T, Hochreiter S. 2016. Fast and accurate deep network learning by Exponential Linear Units (ELUs). Proceedings of the 4th International Conference on Learning Representations (ICLR 2016); May 2-4; San Juan, Puerto Rico.
   Google Scholar
• Costea D, Leordeanu M. 2016. Aerial image geolocalization from recognition and matching of roads and intersections. Proceedings of the British Machine Vision Conference (BMVC 2016); September 19-22; York, UK.
   Google Scholar
• He H, Yang D, Wang S, Zheng Y, Wang S. 2019. Light encoder–decoder network for road extraction of remote sensing images. J Appl Rem Sens. 13(03):1.
   Google Scholar
• Hung YTW, Schulter S, Sohn K. 2018. Learning to adapt structured output space for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018); June 18-22; Salt Lake City, UT, USA. p. 7472–7481.
   Google Scholar
• Ioffe S, Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, (SICML 2015); July 6-11; Lille, France. p. 448–456.
   Google Scholar
• Isola P, Zhu JY, Zhou T, Efros AA. 2017. Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017); July 21-26; Honolulu, HI, USA. p. 5967–5976.
   Google Scholar
• Kingma DP, Ba J. 2015. Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015); May 7-9; San Diego, CA, USA.
   Google Scholar
• Li Y, Yuan L, Vasconcelos N. 2019. Bidirectional learning for domain adaptation of semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019); June 16-20; Long Beach, CA, USA. p. 6936–6945.
   Google Scholar
• Long J, Shelhamer E, Darrell T. 2015. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015); June 7-12; Boston, MA, USA. p. 3431–3440.
   Google Scholar
• Lu X, Zhong Y, Zheng Z, Liu Y, Zhao J, Ma A, Yang J. 2019. Multi-scale and multi-task deep learning framework for automatic road extraction. IEEE Trans Geosci Remote Sens. 57(11):9362–9377.
   Web of Science ®Google Scholar
• Mnih V, Hinton GE. 2010. Learning to detect roads in high-resolution aerial images. Proceedings of the 11th European Conference on Computer Vision (ECCV 2010); September 5-11; Berlin, Heidelberg. p. 210–223.
   Google Scholar
• Mnih V. 2013. Machine learning for aerial image labeling. Toronto: University of Toronto.
   Google Scholar
• Panboonyuen T, Jitkajornwanich K, Lawawirojwong S, Srestasathiern P, Vateekul P. 2017. Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields. Remote Sens. 9(7):680.
   Google Scholar
• Singh PP, Garg RD. 2013. Automatic road extraction from high resolution satellite images using adaptive global thresholding and morphological Operations. J Indian Soc Remote Sens. 41(3):631–640.
   Web of Science ®Google Scholar
• Singh PP, Garg RD. 2014. A two-stage framework for road extraction from high-resolution satellite images by using prominent features of impervious surfaces. Int J Remote Sens. 35(24):8074–8107.
   Web of Science ®Google Scholar
• Ronneberger O, Fischer P, Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015); October 5-9; Munich, Germany. p. 234–241.
   Google Scholar
• Tao C, Qi J, Li Y, Wang H, Li H. 2019. Spatial information inference net: road extraction using road-specific contextual information. Isprs J Photogramm Remote Sens. 158:155–166.
   Web of Science ®Google Scholar
• Wang J, Song J, Chen M, Yang Z. 2015. Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine. Int J Remote Sens. 36(12):3144–3169.
   Web of Science ®Google Scholar
• Wang W, Yang N, Zhang Y, Wang F, Cao T, Eklund P. 2016. A review of road extraction from remote sensing images. J Traffic Transp Eng. 3(3):271–282.
   Google Scholar
• Wegner JD, Montoya-Zegarra JA, Schindler K. 2013. A higher-order crf model for road network extraction. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013); June23-28; Portland, OR, USA. p. 1698–1705.
   Google Scholar
• Wei Y, Wang Z, Xu M. 2017. Road structure refined CNN for road extraction in aerial image. IEEE Geosci Remote Sens Lett. 14(5):709–713.
   Web of Science ®Google Scholar
• Wiedemann C, Heipke C, Mayer H, Jamet O. 1998. Empirical evaluation of automatically extracted road axes. Empirical evaluation techniques in computer vision. Los Alamitos: IEEE Computer Society Press; p. 172–187.
   Google Scholar
• Yang X, Li X, Ye Y, Lau RYK, Zhang X, Huang X. 2019. Road detection and centerline extraction via deep recurrent convolutional neural network U-Net. IEEE Trans Geosci Remote Sens. 57(9):7209–7220.
   Web of Science ®Google Scholar
• Zhang Y, Xiong Z, Zang Y, Wang C, Li J, Li X. 2019. Topology-aware road network extraction via multi-supervised generative adversarial networks. Remote Sens. 11(9):1017.
   Google Scholar
• Zhong Z, Li J, Cui W, Jiang H. 2016. Fully convolutional networks for building and road extraction: preliminary results. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS); July 10-15; Beijing, China. p. 1591–1594.
   Google Scholar
• Zhu JY, Park T, Isola P, Efros AA. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017); October 22-29; Venice, Italy. p. 2242–2251.
   Google Scholar