SAR image water extraction using the attention U-net and multi-scale level set method: flood monitoring in South China in 2020 as a test case

References

• Annarita, D., R. Alberto, P. Guido, F. P. Lovergine, C. Domenico, and M. Salvatore. 2016. “A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data.” IEEE Transactions on Geoscience and Remote Sensing 54 (6): 3612–3625. doi:10.1109/TGRS.2016.2520487.
   Web of Science ®Google Scholar
• Ayed, I. B., C. Vazquez, A. Mitiche, and Z. Belhadj. 2004. “SAR Image Segmentation with Active Contours and Level Sets.” IEEE International Conference on Image Processing 4: 2717–2720. doi:10.1109/ICIP.2004.1421665.
   Google Scholar
• Ayed, I. B. S., and A. Mitiche. 2005. “Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (5): 793–800. doi:10.1109/TPAMI.2005.106.
   PubMed Web of Science ®Google Scholar
• Badrinarayanan, V., A. Kendall, and R. Cipolla. 2017. “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12): 1–14. doi:10.1109/TPAMI.2016.2644615.
   Web of Science ®Google Scholar
• Berman, M., A. R. Triki, and M. B. Blaschko. 2018. “The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks.” Paper presented at the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi:10.1109/CVPR.2018.00464
   Google Scholar
• Biswajeet, P., S.T. Mahyat, and N.J. Mustafa. 2016. “A New Semiautomated Detection Mapping of Flood Extent from TerraSAR-X Satellite Image Using Rule-Based Classification and Taguchi Optimization Techniques.” IEEE Transactions on Geoscience and Remote Sensing 54 (7): 4331–4342. doi:10.1109/TGRS.2016.2539957.
   Web of Science ®Google Scholar
• Chakravarty, D., and D. Pradhan. 2020. “A Level Set Approach for Segmentation of Intensity Inhomogeneous Image Based on Region Decomposition.” SN Computer Science 1 (5): 1–12. doi:10.1007/s42979-020-00278-w.
   Google Scholar
• Chan, T. F., and L. A. Vese. 2001. “Active Contours without Edges.” IEEE Transactions on Image Processing 10 (2): 266–277. doi:10.1109/83.902291.
   PubMed Web of Science ®Google Scholar
• Chen, L. C., G. Papandreou, F. Schroff, and H. Adam. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” arXiv Preprint arXiv:1706.05587.
   Google Scholar
• Chen, L. C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2014. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.” Computer Vision and Pattern Recognition 4: 357–361.
   Google Scholar
• Donato, A., D.M. Gerardo, I. Antonio, R. Daniele, and R. Giuseppe. 2018. “Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images.” IEEE Transactions on Geoscience and Remote Sensing 56 (6): 3290–3299. doi:10.1109/TGRS.2018.2797536.
   Web of Science ®Google Scholar
• Huang, B., H. Li, and X. Huang. 2005. “A Level Set Method for Oil Slick Segmentation in SAR Images.” International Journal of Remote Sensing 26 (6): 1145–1156. doi:10.1080/01431160512331326747.
   Web of Science ®Google Scholar
• Lauko, I. G., A. Honts, J. Beihoff, and S. Rupprecht. 2020. “Local Color and Morphological Image Feature Based Vegetation Identification and Its Application to Human Environment Street View Vegetation Mapping, or How Green Is Our County?” Geo-spatial Information Science 23 (3): 222–236. doi:10.1080/10095020.2020.1805367.
   Web of Science ®Google Scholar
• Lee, J. S., M. R. Grunes, E. Pottier, and Laurent Ferrofamil. 2001. “Segmentation of Polarimetric SAR Images.” IEEE 2001 International Geoscience and Remote Sensing Symposium 1, 414–416. doi:10.1109/IGARSS.2001.976175
   Google Scholar
• Lee, S-H., and J. K. Seo. 2006. “Level Set-Based Bimodal Segmentation with Stationary Global Minimum.” IEEE Transactions on Image Processing 15 (9): 2843–2852. doi:10.1109/TIP.2006.877308.
   PubMed Web of Science ®Google Scholar
• Li, Y., S. Martinis, S. Plank, and R. Ludwig. 2018. “An Automatic Change Detection Approach for Rapid Flood Mapping in Sentinel-1 SAR Data.” International Journal of Applied Earth Observation and Geoinformation 73: 123–135. doi:10.1016/j.jag.2018.05.023.
   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:10.1109/CVPR.2015.7298965.
   Web of Science ®Google Scholar
• Ma, X., S. Liu, S. Hu, P. Geng, M. Liu, and J. Zhao. 2018. “SAR Image Edge Detection via Sparse Representation.” Soft Computing 22 (8): 2507–2515. doi:10.1007/s00500-017-2505-y.
   Web of Science ®Google Scholar
• Meng, Q., X. Wen, L. Yuan, J. Liu, and H. Xu. 2018. “Local Region-Based Level Set Approach for Fast Synthetic Aperture Radar Image Segmentation.” Journal of Applied Remote Sensing 12 (1): 1. doi:10.1117/1.JRS.12.015002.
   Google Scholar
• Mumford, D., and J. Shah. 1989. “Optimal approximation by piecewise smooth functions and associated variational problems.” Communications on Pure and Applied Mathematics 42(4): 577–685. doi:10.1002/cpa.3160420503.
   Google Scholar
• Oktay, O., J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. Mcdonagh, N. Y. Hammerla, and B. Kainz. 2018. “Attention U-Net: Learning Where to Look for the Pancreas.” 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands
   Google Scholar
• Paszke, A., S. Gross, S. Chintala, G. Chanan, E. Yang, Z. Devito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. 2017. “Automatic Differentiation in PyTorch.” 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA
   Google Scholar
• Shi, Z., L. She, L. Li, and Z. Hua. 2013. “A Modified Fuzzy C-Means for Bias Field Estimation and Segmentation of Brain MR Image.” Paper presented at the Control and Decision Conference (CCDC), pp.2080–2085. doi:10.1109/CCDC.2013.6561279
   Google Scholar
• Shuai, Y., S. Hong, and X. Ge. 2008. “SAR Image Segmentation Based on Level Set with Stationary Global Minimum.” IEEE Geoscience and Remote Sensing Letters 5 (4): 644–648. doi:10.1109/LGRS.2008.2001768.
   Web of Science ®Google Scholar
• Silveira, M, and S Heleno. 2009. “Separation between Water and Land in SAR Images Using Region-Based Level Sets.” IEEE Geoscience & Remote Sensing Letters 6 (3): 471–475. doi:10.1109/LGRS.2009.2017283.
   Web of Science ®Google Scholar
• Yu, F., and V. Koltun 2016. “Multi-Scale Context Aggregation by Dilated Convolutions.” the International Conference on Learning Representations (ICLR). San Juan, Puerto Rico.
   Google Scholar
• Yy, A, A Rx, A Wj, A Zc, A Yy, B Lx, and B Bxj. 2021. “Accurate and Automatic Tooth Image Segmentation Model with Deep Convolutional Neural Networks and Level Set Method.” Neurocomputing 419: 108–125. doi:10.1016/j.neucom.2020.07.110.
   Web of Science ®Google Scholar
• Zhang, K., L. Zhang, Kin Man Lam, and D. Zhang. 2016. “A Level Set Approach to Image Segmentation with Intensity Inhomogeneity.” IEEE Transactions on Cybernetics 46 (2): 546–557. doi:10.1109/TCYB.2015.2409119.
   PubMed Web of Science ®Google Scholar
• Zhong, Y. F., X. Y. Wang, S. Y. Wang, and L. P. Zhang. 2021. “Advances in Spaceborne Hyperspectral Remote Sensing in China.” Geo-spatial Information Science 24 (1): 95–120. doi:10.1080/10095020.2020.1860653.
   Web of Science ®Google Scholar
• Zhou, Z., MMR Siddiquee, N. Tajbakhsh, and J. Liang. 2020. “UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation.” IEEE Transactions on Medical Imaging 39 (6): 1856–1867. doi:10.1109/TMI.2019.2959609.
   PubMed Web of Science ®Google Scholar