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Article

DEM Void Filling Based on Context Attention Generation Model

1
College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
2
Schools of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 734; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120734
Received: 15 September 2020 / Revised: 26 November 2020 / Accepted: 5 December 2020 / Published: 7 December 2020
The digital elevation model (DEM) generates a digital simulation of ground terrain in a certain range with the usage of 3D point cloud data. It is an important source of spatial modeling information. Due to various reasons, however, the generated DEM has data holes. Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training, so as to perceive the overall known elevation information, in combination with the contextual attention layer, and generate data with reliability to fill the void areas. The experimental results have managed to show that this method has good feature expression and reconstruction accuracy in DEM void filling, which has been proven to be better than that illustrated by the traditional interpolation method. View Full-Text
Keywords: digital elevation model; void filling; deep learning; deep generative model; context attention layer digital elevation model; void filling; deep learning; deep generative model; context attention layer
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MDPI and ACS Style

Zhang, C.; Shi, S.; Ge, Y.; Liu, H.; Cui, W. DEM Void Filling Based on Context Attention Generation Model. ISPRS Int. J. Geo-Inf. 2020, 9, 734. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120734

AMA Style

Zhang C, Shi S, Ge Y, Liu H, Cui W. DEM Void Filling Based on Context Attention Generation Model. ISPRS International Journal of Geo-Information. 2020; 9(12):734. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120734

Chicago/Turabian Style

Zhang, Chunsen, Shu Shi, Yingwei Ge, Hengheng Liu, and Weihong Cui. 2020. "DEM Void Filling Based on Context Attention Generation Model" ISPRS International Journal of Geo-Information 9, no. 12: 734. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120734

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