Cross-modal change detection flood extraction based on convolutional neural network

Audebert, N., Le Saux, B., Lefèvre, S., 2018. Beyond RGB: very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote Sens. 140 (June 2018): 20–32. doi:10.1016/j.isprsjprs.2017.11.011. Bai, 2021, Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: demonstration of sen1floods11 benchmark datasets, Remote Sens., 13, 10.3390/rs13112220 Bauer-Marschallinger, 2022, Satellite-based flood mapping through bayesian inference from a sentinel-1 SAR datacube, Remote Sens., 14, 10.3390/rs14153673 Bioresita, 2019, Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping, Int. J. Remote Sens., 40, 9026 Chang, X., Deng, B., Bao, Z., Guo, X., Yuan, F., 2022. A Modified D-LinkNet for Water Extraction from High-Resolution Remote Sensing. In:Proceedings of the International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), pp. 151–156. doi:10.1109/AEMCSE55572.2022.00038. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H., 2017. Rethinking Atrous Convolution for Semantic Image Segmentation. ArXiv abs/1706.05587. <http://arxiv.org/abs/1706.05587>. Clement, 2018, Multi-temporal synthetic aperture radar flood mapping using change detection, J. Flood Risk Manage., 11, 152, 10.1111/jfr3.12303 Gan, 2012, Flood mapping of Danube River at Romania using single and multi-date ERS2-SAR images, Int. J. Appl. Earth Obs. Geoinf., 18, 69 Han, D., Kim, J., Kim, J., 2017. Deep pyramidal residual networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6307–6315. doi:10.1109/CVPR.2017.668. Hafner, 2022, Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net, IEEE Geosci. Remote Sens. Lett., 19, 1, 10.1109/LGRS.2021.3119856 He, K., Zhang, X., Ren, S., Sun, J., 2016. Identity Mappings in Deep Residual Networks. In: Proceedings of European Conference on Computer Vision, pp. 630–645. doi:10.48550/arXiv.1603.05027. Hosseinpour, 2022, CMGFNet: a deep cross-modal gated fusion network for building extraction from very high-resolution remote sensing images, ISPRS J. Photogramm. Remote Sens., 184, 96, 10.1016/j.isprsjprs.2021.12.007 Hostache, 2012, Change detection approaches for flood extent mapping: how to select the most adequate reference image from online archives?, Int. J. Appl. Earth Obs. Geoinf., 19, 205 Huang, 2022, AlignSeg: feature-aligned segmentation networks, IEEE Trans. Pattern Anal. Mach. Intell., 44, 550 Konapala, 2021, Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning, ISPRS J. Photogramm. Remote Sens., 180, 163, 10.1016/j.isprsjprs.2021.08.016 Kool, 2022, Seasonal inundation dynamics and water balance of the Mara Wetland, Tanzania based on multi-temporal Sentinel-2 image classification, Int. J. Appl. Earth Obs., 109 Li, 2022, MCANet: a joint semantic segmentation framework of optical and SAR images for land use classification, Int. J. Appl. Earth Obs. Geoinf., 106 Liu, 2022, Fusing landsat-8, sentinel-1, and sentinel-2 data for river water mapping using multidimensional weighted fusion method, IEEE Trans. Geosci. Remote Sens., 60 Niu, 2009, Geographical characteristics of China's wetlands derived from remotely sensed data, Sci. China Ser. D: Earth Sci., 52, 723, 10.1007/s11430-009-0075-2 Peng, 2021, SCDNET: a novel convolutional network for semantic change detection in high resolution optical remote sensing imagery, Int. J. Appl. Earth Obs. Geoinf., 103 Saha, 2022, Self-supervised multisensor change detection, IEEE Trans. Geosci. Remote Sens., 60, 1 Saha, 2022, Supervised change detection using prechange optical-SAR and postchange SAR Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15, 8170, 10.1109/JSTARS.2022.3206898 Son, 2019, Flood assessment using multi-temporal remotely sensed data in Cambodia, Geocarto Int., 36, 1044, 10.1080/10106049.2019.1633420 Song, 2021, SUACDNet: attentional change detection network based on siamese U-shaped structure, Int. J. Appl. Earth Obs. Geoinf., 105 Tan, M.X., Le., Q.V., 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv:1905.11946 [Cs, Stat]. <http://arxiv.org/abs/1905.11946>. Tong, 2018, An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery, ISPRS J. Photogramm. Remote Sens., 136, 144, 10.1016/j.isprsjprs.2017.11.006 Twigg, J., 2004. Disaster risk reduction: mitigation and preparedness in development and emergency programming. Overseas Development Institute (ODI). ISBN/ISSN/DOI:0850036941. Wang, 2021, ACFNet: a feature fusion network for glacial lake extraction based on optical and synthetic aperture radar images, Remote Sens., 13, 10.3390/rs13245091 Wang, 2018, Improving super-resolution flood inundation mapping for multispectral remote sensing image by supplying more spectral information, IEEE Geosci. Remote Sens. Lett., 16, 771, 10.1109/LGRS.2018.2882516 Wang, 2022, A high-resolution feature difference attention network for the application of building change detection, Int. J. Appl. Earth Obs. Geoinf., 112 Ward, 2014, Floodplain inundation and vegetation dynamics in the Alligator Rivers region (Kakadu) of northern Australia assessed using optical and radar remote sensing, Remote Sens. Environ., 147, 43, 10.1016/j.rse.2014.02.009 Wang, 2021, Mask DeepLab: end-to-end image segmentation for change detection in high-resolution remote sensing images, Int. J. Appl. Earth Obs. Geoinf., 104 Youssef, A.M., Pradhan, B., Dikshit, A., Mahdi, A.M., 2022. Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt. Geocarto. Int. doi:10.1080/10106049.2022.2046866. Yuan, Y., Chen, X., Chen, X., Wang, J., 2020. Object-contextual representations for semantic segmentation. In: Proceedings of European Conference on Computer Vision, pp. 173–190. doi:10.1007/978-3-030-58539-6_11. Zhao, 2023, Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network, Int. J. Appl. Earth Obs. Geoinf., 116 Zhao, H.S., Shi, J.P., Qi, X.J., Wang, X.G., Jia, J.Y., 2017. Pyramid scene parsing network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6230–6239. <http://arxiv.org/abs/1612.01105>. Zhang, 2022, Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks, Int. J. Appl. Earth Obs. Geoinf., 112 Zhou, L., Zhang, C., Wu, M., 2018. D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 192–196. doi:10.1109/CVPRW.2018.00034.