D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

Tarboton DG. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour Res. 1997;33(2):309–19. Li J, Wong DWS. Effects of DEM sources on hydrologic applications. Comput Environ Urban Syst. 2010;34.3:251–61. Priestnall G, Jaafar J, Duncan A. Extracting urban features from LiDAR digital surface models. Comput Environ Urban Syst. 2000;24(2):65–78. Florinsky I. Digital terrain analysis in soil science and geology. Cambridge: Academic Press; 2016. Fisher PF, Tate NJ. Causes and consequences of error in digital elevation models. ProgPhysGeogr. 2006;30(4):467–89. Liu X. Airborne LiDAR for DEM generation: some critical issues. ProgPhysGeogr. 2008;32(1):31–49. Woodrow K, Lindsay JB, Berg AA. Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction. J Hydrol. 2016;540:1022–9. Heritage GL, et al. Influence of survey strategy and interpolation model on DEM quality. Geomorphology. 2009;112.3-4:334–44. Sørensen R, Seibert J. Effects of DEM resolution on the calculation of topographical indices: TWI and its components. J Hydrol. 2007;347(1–2):79–89. Chaubey I, et al. Effect of DEM data resolution on SWAT output uncertainty. Hydrol Process Int J. 2005;19(3):621–8. Sanders BF. Evaluation of on-line DEMs for flood inundation modeling. Adv Water Resour. 2007;30(8):1831–1843. https://doi.org/10.1016/j.advwatres.2007.02.005. Li Z, Mount J, Demir I. Evaluation of model parameters of HAND model for real-time flood inundation mapping: iowa case study. EarthArXiv. 2020. Zhang W, Montgomery DR. Digital elevation model grid size, landscape representation, and hydrologic simulations. Water Resour Res. 1994;30(4):1019–28. Claessens L, et al. DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surface Process Landf. 2005;30(4):461–77. Sit M, Demir I. Decentralized flood forecasting using deep neural networks. 2019; arXiv preprint http://arxiv.org/abs/1902.02308. Xiang Z, Yan J, Demir I. A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning. Water Resour Res. 2019;56(1):e2019WR025326. https://doi.org/10.1029/2019WR025326. Xiang Z, Demir I. Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa. Environ ModelSoftw. 2020;131:104761. https://doi.org/10.1016/j.envsoft.2020.104761. Vaze J, Teng J, Spencer G. Impact of DEM accuracy and resolution on topographic indices. Environ ModelSoftw. 2010;25(10):1086–98. Sit M, Sermet Y, Demir I. Optimized watershed delineation library for server-side and client-side web applications. Open Geospat Data, Softw Stand. 2019;4(1):8. Demir I, Szczepanek R. Optimization of river network representation data models for web-based systems. Earth Space Sci. 2017;4(6):336–47. Sermet Y, et al. Crowdsourced approaches for stage measurements at ungauged locations using smartphones. Hydrol Sci J. 2019;65(5):1–10. https://doi.org/10.1080/02626667.2019.1659508. Saksena S, Merwade V. Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. J Hydrol. 2015;530:180–94. Xu Z, et al. Nonlocal similarity based DEM super resolution. ISPRS J Photogramm Remote Sens. 2015;110:48–54. Sit, M, et al. A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol. 2020. Dong C, et al. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;382:295–307. Chen, Z, Wang X, Xu Z. Convolutional neural network based DEM Super resolution. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2016;41:247–250. https://doi.org/10.5194/isprs-archives-XLI-B3-247-2016. Ledig C, et al. Photo-realistic single image super-resolution using a generative adversarial network. CVPR. 2017;2(3):105–114. https://doi.org/10.1109/CVPR.2017.19. Xu Z, et al. Deep gradient prior network for DEM super-resolution: transfer learning from image to DEM. ISPRS J Photogramm Remote Sens. 2019;150:80–90. Nasrollahi K, Moeslund TB. Super-resolution: a comprehensive survey. Mach Vis Appl. 2014;25.6:1423–68. Bevilacqua M. Algorithms for super-resolution of images and videos based on learning methods. Diss. 2014. Farsiu S, et al. Fast and robust multiframe super resolution. IEEE Trans Image Process. 2004;1310:1327–44. Irani M, Peleg S. Improving resolution by image registration. CVGIP Graph Models Image Process. 1991;533:231–9. Jung C, et al. Position-patch based face hallucination using convex optimization. IEEE Signal Process Lett. 2011;18.6:367–70. Yang, C-Y, Ma C, Yang M-H. Single-image super-resolution: A benchmark. In: European Conference on computer vision. Springer, Cham, 2014. Takeda H, Farsiu S, Milanfar P. Kernel regression for image processing and reconstruction. IEEE Trans Image Process. 2007;16(2):349–66. Protter M, et al. Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans Image Process. 2008;18(1):36–51. Lin Z, Shum H-Y. Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans Pattern Anal Mach Intell. 2004;26(1):83–97. Li Y, et al. Single image super-resolution reconstruction based on genetic algorithm and regularization prior model. Inform Sci. 2016;372:196–207. Chang H, Yeung D-Y, Xiong Y. Super-resolution through neighbor embedding. Computer Vision and Pattern Recognition, 2004. In: CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on. Vol. 1. IEEE, 2004. Yang J, et al. Image super-resolution via sparse representation. IEEE Trans Image Process. 2010;19.11:2861–73. Ni KS, Nguyen TQ. Image superresolution using support vector regression. IEEE Trans Image Process. 2007;16(6):1596–610. Kim KI, Kwon Y. Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell. 2010;6:1127–33. Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. BiolCybern. 1980;36(4):193–202. LeCun, Y, et al. Object recognition with gradient-based learning. In: David AF, Joseph LM, Vito di Ges´u, Roberto Cipolla, editors. Shape, contour and grouping in computer vision. Berlin: Springer; 1999, p. 319–45. https://link-springer-com.proxy.lib.uiowa.edu/book/10.1007%2F3-540-46805-6#toc. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. Curran Associates, Inc; 2012. p. 1097–1105. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf. Lawrence S, et al. Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw. 1997;8.1:98–113. Dong C, Loy CC, Tang X. Accelerating the super-resolution convolutional neural network. In: European Conference on computer vision. Springer, Cham, 2016. Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2016. Kim J, Lee JK, Lee KM. Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2016. Shi, W, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. Lim B, et al. "nhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition workshops. 2017. Zhang Y, et al. "Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2018. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, editors. Advances in neural information processing systems. Curran Associates, Inc; 2014. p. 2672–2680. https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf. Isola P, et al. Image-to-image translation with conditional adversarial networks. 2017; arXiv preprint. Perarnau G, et al. Invertible conditional gans for image editing. 2016; arXiv preprint http://arxiv.org/abs/1611.06355. Wang Y, et al. A fully progressive approach to single-image super-resolution. 2018; arXiv preprint http://arxiv.org/abs/1804.02900. Mahapatra D. Retinal vasculature segmentation using local saliency maps and generative adversarial networks for image super resolution. 2017; arXiv preprint http://arxiv.org/abs/1710.04783 Wang X, et al. Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV). 2018. Argudo O, Chica A, Andujar C. Terrain super‐resolution through aerial imagery and fully convolutional networks. Comput Graphics Forum. 2018;37(2):101–110. https://doi.org/10.1111/cgf.13345. Yue L, et al. "Fusion of multi-scale DEMs using a regularized super-resolution method. Int J GeographInfSci. 2015;29.12:2095–120. Nair V, Hinton GE. "Rectified linear units improve restricted Boltzmann machines. In: ICML. 2010. He K, et al. Identity mappings in deep residual networks. In: European Conference on computer vision. Springer, Cham, 2016. Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014; arXiv preprint http://arxiv.org/abs/1412.6980. Tang J, Pilesjö P. Estimating slope from raster data: a test of eight different algorithms in flat, undulating and steep terrain. River Basin Manag. 2011;VI:143–154. https://doi.org/10.2495/RM110131. Bolstad PV, Stowe T. An evaluation of DEM accuracy: elevation, slope, and aspect. PhotogrammEng Remote Sens. 1994;60(11):1327–32. Burrough PA, et al. Principles of geographical information systems. Oxford: Oxford University Press; 2015. Kingma DP, Welling W. Auto-encoding variational bayes. 2013. arXiv preprint http://arxiv.org/abs/1312.6114.