Building extraction from VHR remote sensing imagery by combining an improved deep convolutional encoder-decoder architecture and historical land use vector map

References

• Abraham, N., and N. Khan. 2018. “A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation.” arXiv Preprint:1810.07842. https://arxiv.org/pdf/1810.07842
   Google Scholar
• Ahmadi, S., M. Zoej, H. Ebadi, H. Moghaddam, and A. Mohammadzadeh. 2010. “Automatic Urban Building Boundary Extraction from High Resolution Aerial Images Using an Innovative Model of Active Contours.” International Journal of Applied Earth Observations & Geoinformation 12 (3): 150–157. doi:10.1016/j.jag.2010.02.001.
   Web of Science ®Google Scholar
• Alom, M., M. Hasan, C. Yakopcic, T. Taha, and V. Asari. 2018. “Recurrent Residual Convolutional Neural Network Based on U-Net (R2u-net) for Medical Image Segmentation.” arXiv Preprint:1802.06955. https://arxiv.org/ftp/arxiv/papers/1802/1802.06955
   Google Scholar
• Azimi, S., P. Fischer, M. Körner, and P. Reinartz. 2019. “Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 57 (5): 2920–2938. doi:10.1109/TGRS.2018.2878510.
   Web of Science ®Google Scholar
• Badrinarayanan, V., A. Kendall, and R. Cipolla. 2015. “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12): 2481–2495. doi:10.1109/TPAMI.2016.2644615.
   Web of Science ®Google Scholar
• Bischke, B., P. Helber, J. Folz, D. Borth, and A. Dengel. 2017. “Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks.” arXiv Preprint:1709.05932. http://cn.arxiv.org/abs/1709.05932
   Google Scholar
• Buslaev, A., S. Seferbekov, V. Iglovikov, and A. Shvets. 2018. “Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery.” arXiv Preprint: 1806.05182. https://arxiv.org/abs/1806.05182
   Google Scholar
• Chen, G., X. Zhang, Q. Wang, F. Dai, Y. Gong, and K. Zhu. 2018. “Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 11 (5): 1633–1644. doi:10.1109/JSTARS.2018.2810320.
   Web of Science ®Google Scholar
• Chen, L., G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille. 2016. “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–848. doi:10.1109/TPAMI.2017.2699184.
   Web of Science ®Google Scholar
• Chen, Q., L. Wang, Y. Wu, G. Wu, Z. Guo, and S. Waslander. 2019. “Aerial Imagery for Roof Segmentation: A Large-scale Dataset Towards Automatic Mapping of Buildings.” ISPRS Journal of Photogrammetry and Remote Sensing 147: 42–55. doi:10.1016/j.isprsjprs.2018.11.011.
   Web of Science ®Google Scholar
• Chollet, F. 2015. “Keras.” GitHub. San Francisco, CA. https://github.com/fchollet/keras
   Google Scholar
• Dai, J., K. He, and J. Sun. 2016. “Instance-aware Semantic Segmentation via Multi-task Network Cascades.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/CVPR.2016.343.
   Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative Adversarial Nets.” International Conference on Neural Information Processing Systems. doi:10.1017/CBO9781139058452.
   Google Scholar
• Hamed, A., and A. Hossein. 2019. “Height Estimation from Single Aerial Images Using a Deep Convolutional Encoder-decoder Network.” ISPRS Journal of Photogrammetry and Remote Sensing 149: 50–66. doi:10.1016/j.isprsjprs.2019.01.013.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” IEEE Conference Computer Visual and Pattern Recognition (CVPR). doi:10.1109/CVPR.2016.90.
   Google Scholar
• Huang, G., Z. Liu, L. Maaten, and K. Weinberger. 2017. “Densely Connected Convolutional Networks.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/CVPR.2017.243.
   Google Scholar
• Huang, X., and L. Zhang. 2011. “A Multidirectional and Multi-scale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery.” Photogrammetric Engineering and Remote Sensing 77 (7): 721–732. doi:10.14358/PERS.77.7.721.
   Web of Science ®Google Scholar
• Huang, X., and L. Zhang. 2012. “Morphological Building/Shadow Index for Building Extraction from High-Resolution Imagery over Urban Areas.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 5 (1): 161–172. doi:10.1109/jstars.2011.2168195.
   Web of Science ®Google Scholar
• Iglovikov, V., and A. Shvets. 2018. “TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation.” arXiv Preprint:1801.05746. https://arxiv.org/abs/1801.05746v1
   Google Scholar
• Jégou, S., M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio. 2017. “The One Hundred Layers Tiramisu: Fully Convolutional Densenets for Semantic Segmentation.” IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR). doi:10.1109/CVPRW.2017.156.
   Google Scholar
• Ji, S., S. Wei, and M. Lu. 2018. “Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set.” IEEE Transactions on Geoscience & Remote Sensing 57 (1): 574–586. doi:10.1109/TGRS.2018.2858817.
   Web of Science ®Google Scholar
• Li, E., S. Xu, W. Meng, and X. Zhang. 2017. “Building Extraction from Remotely Sensed Images by Integrating Saliency Cue.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 10 (3): 906–919. doi:10.1109/JSTARS.2016.2603184.
   Web of Science ®Google Scholar
• Lin., L., J. Liang, M. Weng, and H. Zhu. 2018. “A Multiple-Feature Reuse Network to Extract Buildings from Remote Sensing Imagery.” Remote Sensing 10 (9): 1350–1360. doi:10.3390/rs10091350.
   Web of Science ®Google Scholar
• Long, J., E. Shelhamer, and T. Darrell. 2014. “Fully Convolutional Networks for Semantic Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4): 640–651. doi:10.1109/TPAMI.2016.2572683.
   Web of Science ®Google Scholar
• Maggiori, E., Y. Tarabalka, G. Charpiat, and P. Alliez. 2017a. “Convolutional Neural Networks for Large-scale Remote-sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (2): 645–657. doi:10.1109/TGRS.2016.2612821.
   Web of Science ®Google Scholar
• Maggiori, E., Y. Tarabalka, G. Charpiat, and P. Alliez. 2017b. “Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark.” IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Fort Worth, United States. doi:10.1109/IGARSS.2017.8127684.
   Google Scholar
• Mayunga, S., D. Coleman, and Y. Zhang. 2010. “Semi-automatic Building Extraction in Dense Urban Settlement Areas from High-resolution Satellite Images.” Empire Survey Review 42 (315): 50–61. doi:10.1179/003962609X451690.
   Web of Science ®Google Scholar
• Mnih, V. 2013. “Machine Learning for Aerial Image Labeling.” Ph.D. Thesis, University of Toronto, Toronto, ON.
   Google Scholar
• Mou, L., and X. Zhu. 2018. “RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images.” arXiv Preprint: 1805.02091. http://cn.arxiv.org/pdf/1805.02091
   Google Scholar
• Muhammad, S., M. Michael, F. Friedrich, Y. Wang, and X. Zhu. 2018. “Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 1100–1116. doi:10.1109/tgrs.2018.2864716.
   Web of Science ®Google Scholar
• Nicolas, A., L. Bertrand, and L. Sébastien. 2016. “Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks.” Asian Conference on Computer Vision (ACCV) 10111: 180–196. doi:10.1007/978-3-319-54181-5_12.
   Google Scholar
• Oktay, O., J. Schlemper, L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, et al. 2018. “Attention U-Net: Learning Where to Look for the Pancreas.” arXiv Preprint: 1804.03999. https://arxiv.org/pdf/1804.03999
   Google Scholar
• Penrose, L. 1946. “The Elementary Statistics of Majority Voting.” Journal of the Royal Statistical Society 109 (1): 53–57. doi:10.2307/2981392.
   Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 9351: 234–241. doi:10.1007/978-3-319-24574-4_28.
   Google Scholar
• Shahzad, M., M. Maurer, F. Fraundorfer, Y. Wang, and X. Zhu. 2018. “Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 1–17. doi:10.1109/TGRS.2018.2864716.
   Web of Science ®Google Scholar
• Shrestha, S., and L. Vanneschi. 2018. “Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction.” Remote Sensing 10 (7): 1135–1155. doi:10.3390/rs10071135.
   Web of Science ®Google Scholar
• Simonyan, A., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-scale Image Recognition.” arXiv Preprint:1409.1556. https://arxiv.org/pdf/1409.1556.pdf
   Google Scholar
• Vladimir, I., and S. Alexey. 2018. “TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation.” arXiv Preprint: 1801.05746. https://arxiv.org/abs/1801.05746
   Google Scholar
• Wang, X., S. Liu, P. Du, H. Liang, J. Xia, and Y. Li. 2018. “Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning.” Remote Sensing 10 (2): 276–290. doi:10.3390/rs10020276.
   Web of Science ®Google Scholar
• Wu, G., X. Shao, Z. Guo, Q. Chen, W. Yuan, X. Shi, Y. Xu, and R. Shibasaki. 2018. “Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks.” Remote Sensing 10 (3): 407–417. doi:10.3390/rs10030407.
   Web of Science ®Google Scholar
• Xu, Y., L. Wu, Z. Xie, and Z. Chen. 2018. “Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters.” Remote Sensing 10 (1): 144. doi:10.3390/rs10010144.
   Web of Science ®Google Scholar
• Yang, H., J. Yuan, D. Lunga, M. Laverdiere, A. Rose, and B. Bhaduri. 2018. “Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (8): 2600–2614. doi:10.1109/JSTARS.2018.2835377.
   Web of Science ®Google Scholar
• Yuan, J. 2018. “Learning Building Extraction in Aerial Scenes with Convolutional Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (11): 2793–2798. doi:10.1109/TPAMI.2017.2750680.
   PubMed Web of Science ®Google Scholar
• Zeiler, D. 2012. “ADADELTA: An Adaptive Learning Rate Method.” arXiv Preprint:1212.5701. https://arxiv.org/abs/1212.5701
   Google Scholar