Semantic segmentation sample augmentation based on simulated scene generation—case study on dock extraction from high spatial resolution imagery

References

• Adamu, A., and E. Eyad. 2019. “MFC-GAN: Class-imbalanced Dataset Classification Using Multiple Fake Class Generative Adversarial Network.” Neurocomputing 361: 212–221. doi:https://doi.org/10.1016/j.neucom.2019.06.043.
   Web of Science ®Google Scholar
• Berthelot, D., T. Schumm, and L. Metz. 2017. “Began: Boundary Equilibrium Generative Adversarial Networks.” arXiv Preprint arXiv: 1703.10717.
   Google Scholar
• Brock, A., J. Donahue, and K. Simonyan. 2019. “Large Scale GAN Training for High Fidelity Natural Image Synthesis.” arXiv Preprint arXiv: 1809.11096v2.
   Google Scholar
• Cheng, G., P. Zhou, and J. Han. 2016. “Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in Vhr Optical Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 54 (12): 7405–7415. doi:https://doi.org/10.1109/TGRS.2016.2601622.
   Web of Science ®Google Scholar
• Cubuk, E. D., B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. 2018. “AutoAugment: Learning Augmentation Policies from Data.” arXiv Preprint arXiv: 1805.09501.
   Google Scholar
• Dong, H., P. Neekhara, C. Wu, and Y. Guo. 2017. “Unsupervised Image-to-image Translation with Generative Adversarial Networks.” arXiv Preprint arXiv: 1701.02676.
   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.” arXiv Preprint arXiv: 1406.2661.
   Google Scholar
• Harris, C. G., and M. Stephens. 1988. “A Combined Corner and Edge Detector.” Paper presented at the Proceedings of the Forth Alvey Vision Conference, Manchester. doi: https://doi.org/10.5244/C.2.23.
   Google Scholar
• Inoue, H. 2018. “Data Augmentation by Pairing Samples for Images Classification.” arXiv Preprint arXiv: 1801.02929.
   Google Scholar
• Isola, P., J. Y. Zhu, T. H. Zhou, and A. A. Efros. 2017. “Image-To-Image Translation with Conditional Adversarial Networks.” Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Coimbatore, India, June 12-14. doi: https://doi.org/10.1109/ICECA.2019.8822195.
   Google Scholar
• Ji, S., S. Wei, and M. Lu. 2018. “A Scale Robust Convolutional Neural Network for Automatic Building Extraction from Aerial and Satellite Imagery.” International Journal of Remote Sensing 40 (9): 3308–3322. doi:https://doi.org/10.1080/01431161.2018.1528024.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.”Paper presented at the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada. 1: 1097–1105. doi: https://doi.org/10.1145/3065386.
   Google Scholar
• Li, R., W. Liu, L. Yang, S. Sun, W. Hu, F. Zhang, and W. Li. 2018a. “DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (11): 3954–3962. doi:https://doi.org/10.1109/JSTARS.2018.2833382.
   Web of Science ®Google Scholar
• Li, S., Y. Chen, Y. Peng, and L. Bai. 2018b. “Learning More Robust Features with Adversarial Training.” arXiv Preprint arXiv: 1804.07757.
   Google Scholar
• Lin, D., K. Fu, Y. Wang, G. Xu, and X. Sun. 2017. “MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification.” IEEE Geoscience and Remote Sensing Letters 14 (11): 2092–2096. doi:https://doi.org/10.1109/LGRS.2017.2752750.
   Web of Science ®Google Scholar
• Litjens, G., T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. Laak, B. Ginneken, and C. I. Sánchez. 2017. “A Survey on Deep Learning in Medical Image Analysis.” Medical Image Analysis 42: 60–88. doi:https://doi.org/10.1016/j.media.2017.07.005.
   PubMed Web of Science ®Google Scholar
• Long, J., E. Shelhamer, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4): 640–651. doi:https://doi.org/10.1109/CVPR.2015.7298965.
   Web of Science ®Google Scholar
• Mirza, M., and S. Osindero. 2014. “Conditional Generative Adversarial Nets.” arXiv Preprint arXiv: 1411.1784.
   Google Scholar
• Peng, D., Y. Zhang, and H. Guan. 2019. “End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++.” Remote Sensing 11 (11): 1382–1405. doi:https://doi.org/10.3390/rs11111382.
   Web of Science ®Google Scholar
• Perez, L., and J. Wang. 2017. “The Effectiveness of Data Augmentation in Image Classification Using Deep Learning.” arXiv Preprint arXiv: 1712.04621.
   Google Scholar
• Radford, A., L. Metz, and S. Chintala. 2016. “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” arXiv Preprint arXiv: 1511.06434.
   Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-net: Convolutional Networks for Biomedical Image Segmentation.” Paper presented at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Munich, Germany. doi: https://doi.org/10.1007/978-3-319-24574-4_28.
   Google Scholar
• Shao, Z., P. Tang, Z. Wang, N. Saleem, S. Yam, and C. Sommai. 2020. “BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction from High-Resolution Remote Sensing Images.” Remote Sensing 12 (6): 1050–1067. doi:https://doi.org/10.3390/rs12061050.
   Web of Science ®Google Scholar
• Shorten, C., and T. Khoshgoftaar. 2019. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data 6 (1): 1–48. doi:https://doi.org/10.1186/s40537-019-0197-0.
   Google Scholar
• Summers, C., and M. J. Dinneen. 2019. “Improved Mixed-Example Data Augmentation.” Paper presented at the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, USA, January 7–11. doi: https://doi.org/10.1109/WACV.2019.00139.
   Google Scholar
• Takahashi, R., T. Matsubara, and K. Uehara. 2019. “Data Augmentation Using Random Image Cropping and Patches for Deep CNNs.” IEEE Transactions on Circuits and Systems for Video Technology 30 (9): 2917–2931. doi:https://doi.org/10.1109/TCSVT.2019.2935128.
   Web of Science ®Google Scholar
• Taylor, L., and G. Nitschke. 2018. “Improving Deep Learning with Generic Data Augmentation.” Paper presented at the 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Bangalore, India, November 18-21, 1542–1547. doi: https://doi.org/10.1109/SSCI.2018.8628742.
   Google Scholar
• Telea, A. 2004. “An Image Inpainting Technique Based on the Fast Marching Method.” Journal of Graphics Tools 9 (1): 23–34. doi:https://doi.org/10.1080/10867651.2004.10487596.
   Google Scholar
• Terrance, V., and W. T. Graham. 2017. “Dataset Augmentation in Feature Space.” arXiv Preprint arXiv: 1702.05538.
   Google Scholar
• Tokozume, Y., Y. Ushiku, and T. Harada. 2018. “Between-class Learning for Image Classification.” Paper presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June 18-23. doi: https://doi.org/10.1109/CVPR.2018.00575.
   Google Scholar
• Verma, V., A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, and Y. Bengio. 2019. “Manifold Mixup: Better Representations by Interpolating Hidden States.” Paper presented at the 36th International Conference on Machine Learning, PMLR, 97:6438–6447. Long Beach, United States.
   Google Scholar
• Volpi, R., P. Morerio, S. Savarese, and V. Murino. 2018. “Adversarial Feature Augmentation for Unsupervised Domain Adaptation.” Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, December 17. doi: https://doi.org/10.1109/CVPR.2018.00576.
   Google Scholar
• Wang, J., J. Huang, M. Wang, and D. Ming. 2018a. “Dock Extraction from China’s Gaofen-2 Multispectral Imagery Based on Region-line Primitive Association Analyses.” International Journal of Remote Sensing 40 (10): 3878–3899. doi:https://doi.org/10.1080/01431161.2018.1553321.
   Web of Science ®Google Scholar
• Wang, M., and J. Wang. 2016. “A Region-line Primitive Association Framework for Object-based Remote Sensing Image Analysis.” Photogrammetric Engineering and Remote Sensing 82 (2): 149–159. doi:https://doi.org/10.14358/PERS.82.2.149.
   Web of Science ®Google Scholar
• Wang, M., J. Xing, J. Wang, and G. Lv. 2017. “Technical Design and System Implementation of Region-line Primitive Association Framework.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 202–216. doi:https://doi.org/10.1016/j.isprsjprs.2017.06.002.
   Web of Science ®Google Scholar
• Wang, T., M. Liu, J. Zhu, A. Tao, J. Kautz, and B. Catanzaro. 2018b. “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs.” Paper presented at the IEEE conference on computer vision and pattern recognition. Salt Lake City, USA. December 17. doi: https://doi.org/10.1109/CVPR.2018.00917.
   Google Scholar
• Wong, S. C., A. Gatt, V. Stamatescu, and M. D. McDonnell. 2016. “Understanding Data Augmentation for Classification: When to Warp?” Paper presented at the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD. doi: https://doi.org/10.1109/DICTA.2016.7797091.
   Google Scholar
• Xiao, C., Y. Li, H. Zhang, and J. Chen. 2020. “Semantic Segmentation of Remote Sensing Image Based on Deep Fusion Networks and Conditional Random Field.” Journal of Remote Sensing (Chinese) 24 (3): 254–264. doi:https://doi.org/10.11834/jrs.20208298.
   Google Scholar
• Xu, M., J. Zhang, B. Ni, and T. Li. 2020. “Adversarial Domain Adaptation with Domain Mixup.” Paper presented at the AAAI Conference on Artificial Intelligence, 34( 4):6502–6509. Hilton New York Midtown, New York, USA. doi: https://doi.org/10.1609/aaai.v34i04.6123.
   Google Scholar
• Yun, S., D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo. 2019. “CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features.” Paper presented at the IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South), October 27– November 2. doi: https://doi.org/10.1109/ICCV.2019.00612.
   Google Scholar
• Zhang, H., M. Cisse, Y. Dauphin, and D. Lopez-Paz. 2017a. “Mixup: Beyond Empirical Risk Minimization.” arXiv Preprint arXiv: 1710.09412.
   Google Scholar
• Zhang, Z., Q. Liu, and Y. Wang. 2017b. “Road Extraction by Deep Residual U-Net.” IEEE Geoscience and Remote Sensing Letters 15 (5): 749–753. doi:https://doi.org/10.1109/LGRS.2018.2802944.
   Web of Science ®Google Scholar
• Zhu, J., T. Park, P. Isola, and A. A. Efros. 2017. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” Paper presented at the IEEE international conference on computer vision. Venice, Italy, October 22-29. doi: https://doi.org/10.1109/ICCV.2017.244.
   Google Scholar