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

LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Elevation Models (DEMs). DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface analysis. A number of studies in flooding suggest the usage of high-resolution DEMs as inputs in the applications improve the overall reliability and accuracy. Despite the importance of high-resolution DEM, many areas in the United States and the world do not have access to high-resolution DEM due to technological limitations or the cost of the data collection. With recent development in Graphical Processing Units (GPU) and novel algorithms, deep learning techniques have become attractive to researchers for their performance in learning features from high-resolution datasets. Numerous new methods have been proposed such as Generative Adversarial Networks (GANs) to create intelligent models that correct and augment large-scale datasets. In this paper, a GAN based model is developed and evaluated, inspired by single image super-resolution methods, to increase the spatial resolution of a given DEM dataset up to 4 times without additional information related to data.

[1]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[3]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[4]  P. Bolstad,et al.  An evaluation of DEM accuracy: elevation, slope, and aspect , 1994 .

[5]  Philippe Gourbesville,et al.  Overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network , 2019, Smart Water.

[6]  Bogdan Raducanu,et al.  Invertible Conditional GANs for image editing , 2016, ArXiv.

[7]  Dwarikanath Mahapatra,et al.  Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution , 2017, ArXiv.

[8]  Yifan Wang,et al.  A Fully Progressive Approach to Single-Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[9]  Liangpei Zhang,et al.  Fusion of multi-scale DEMs using a regularized super-resolution method , 2015, Int. J. Geogr. Inf. Sci..

[10]  Haidawati Nasir,et al.  Singular value decomposition based fusion for super-resolution image reconstruction , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[11]  Ibrahim Demir,et al.  Decentralized Flood Forecasting Using Deep Neural Networks , 2019, ArXiv.

[12]  Igor V. Florinsky,et al.  Digital Terrain Analysis in Soil Science and Geology , 2011 .

[13]  David A. Forsyth,et al.  Shape, Contour and Grouping in Computer Vision , 1999, Lecture Notes in Computer Science.

[14]  Peter F. Fisher,et al.  Causes and consequences of error in digital elevation models , 2006 .

[15]  Bekir Z. Demiray,et al.  D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks , 2021, SN Comput. Sci..

[16]  Jun Yan,et al.  A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning , 2020, Water Resources Research.

[17]  J. Seibert,et al.  Effects of DEM resolution on the calculation of topographical indices: TWI and its components , 2007 .

[18]  Federico Vaggi,et al.  GANs for Biological Image Synthesis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Marco Bevilacqua,et al.  Algorithms for super-resolution of images and videos based on learning methods. (Algorithmes de super-résolution d'images et de vidéos basés sur des méthodes d'apprentissage) , 2014 .

[21]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for hyperspectral images , 2012, Signal Process..

[23]  Chih-Yuan Yang,et al.  Single-Image Super-Resolution: A Benchmark , 2014, ECCV.

[24]  Kwang In Kim,et al.  Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[27]  Ibrahim Demir,et al.  Optimization of river network representation data models for web‐based systems , 2017 .

[28]  Ibrahim Demir,et al.  FLOODSS: Iowa flood information system as a generalized flood cyberinfrastructure , 2018 .

[29]  Bong-Chul Seo,et al.  Real-Time Flood Forecasting and Information System for the State of Iowa , 2017 .

[30]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Yang Wang,et al.  Single image super-resolution reconstruction based on genetic algorithm and regularization prior model , 2016, Inf. Sci..

[32]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[33]  Dacheng Tao,et al.  Single Image Superresolution via Directional Group Sparsity and Directional Features , 2015, IEEE Transactions on Image Processing.

[34]  Aaron A. Berg,et al.  Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction , 2016 .

[35]  P. Pilesjö,et al.  Estimating slope from raster data: a test of eight different algorithms in flat, undulating and steep terrain , 2011 .

[36]  Debiao Li,et al.  Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network , 2018, MICCAI.

[37]  D. Tarboton.  A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .

[38]  Yao Zhao,et al.  Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network , 2017, ArXiv.

[39]  Chen Zixuan,et al.  Nonlocal similarity based DEM super resolution , 2015 .

[40]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[41]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[42]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[43]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[44]  G. Priestnalla,et al.  Extracting urban features from LiDAR digital surface models , 2022 .

[45]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Ibrahim Demir,et al.  An integrated web framework for HAZUS-MH flood loss estimation analysis , 2019, Natural Hazards.

[47]  Maoguo Gong,et al.  Position-Patch Based Face Hallucination Using Convex Optimization , 2011, IEEE Signal Processing Letters.

[48]  Verónica Vilaplana,et al.  Brain MRI super-resolution using 3D generative adversarial networks , 2018, ArXiv.

[49]  W. Krajewski,et al.  The Iowa Watersheds Project: Iowa's prototype for engaging communities and professionals in watershed hazard mitigation , 2018 .

[50]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[51]  Lorenzo Marchi,et al.  Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin , 2012 .

[52]  Venkatesh Merwade,et al.  Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping , 2015 .

[53]  Jin Teng,et al.  Impact of DEM accuracy and resolution on topographic indices , 2010, Environ. Model. Softw..

[54]  Li Jun,et al.  Digital Terrain Analysis Based on DEM , 2005 .

[55]  Ibrahim Demir,et al.  Optimized watershed delineation library for server-side and client-side web applications , 2019, Open Geospatial Data, Software and Standards.

[56]  Ibrahim Demir,et al.  Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma , 2019, Int. J. Digit. Earth.

[57]  Truong Q. Nguyen,et al.  Image Superresolution Using Support Vector Regression , 2007, IEEE Transactions on Image Processing.

[58]  Christopher Kanan,et al.  Aerial Spectral Super-Resolution using Conditional Adversarial Networks , 2017, ArXiv.

[59]  Muhammed Sit,et al.  Realistic River Image Synthesis Using Deep Generative Adversarial Networks , 2020, Frontiers in Water.

[60]  Tomislav Hengl,et al.  Chapter 4 Preparation of DEMs for Geomorphometric Analysis , 2009 .

[61]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[62]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[63]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[64]  Weiguo Gong,et al.  Combining sparse representation and local rank constraint for single image super resolution , 2015, Inf. Sci..

[65]  D. Milan,et al.  Influence of survey strategy and interpolation model on DEM quality , 2009 .

[66]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[67]  Wan-Chi Siu,et al.  Single image super-resolution using Gaussian process regression , 2011, CVPR 2011.

[68]  Ibrahim Demir,et al.  An intelligent system on knowledge generation and communication about flooding , 2018, Environ. Model. Softw..

[69]  David W. S. Wong,et al.  Effects of DEM sources on hydrologic applications , 2010, Comput. Environ. Urban Syst..

[70]  Xu Zekai,et al.  Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[71]  B. Sanders.  Evaluation of on-line DEMs for flood inundation modeling , 2007 .

[72]  Xu Zekai,et al.  Convolutional Neural Network Based dem Super Resolution , 2016 .

[73]  T. A. Costello,et al.  Effect of DEM data resolution on SWAT output uncertainty , 2005 .

[74]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[75]  Yusuf Sermet,et al.  Crowdsourced approaches for stage measurements at ungauged locations using smartphones , 2020, Hydrological Sciences Journal.

[76]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[77]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[78]  Hong Chang,et al.  Super-resolution through neighbor embedding , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[79]  J. Fairfield,et al.  Drainage networks from grid digital elevation models , 1991 .

[80]  Thomas B. Moeslund,et al.  Super-resolution: a comprehensive survey , 2014, Machine Vision and Applications.

[81]  Kyoung Mu Lee,et al.  Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[82]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[83]  Harry Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[84]  Ibrahim Demir,et al.  Flood action VR: a virtual reality framework for disaster awareness and emergency response training , 2019, SIGGRAPH Posters.

[85]  Ibrahim Demir,et al.  Towards an information centric flood ontology for information management and communication , 2019, Earth Science Informatics.

[86]  Xuelong Li,et al.  Single Image Super-Resolution With Multiscale Similarity Learning , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[87]  Xiaoye Liu,et al.  Airborne LiDAR for DEM generation: some critical issues , 2008 .

[88]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[89]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.