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Digital Surface Model Super-Resolution by Integrating High-Resolution Remote Sensing Imagery Using Generative Adversarial Networks

文献类型：期刊论文

作者Sun, Guihou3; Chen, Yuehong3; Huang, Jiamei3; Ma, Qiang1; Ge, Yong2
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2024
卷号17页码:10636-10647
关键词Feature extraction Remote sensing Generators Spatial resolution Surface topography Digital surface model (DSM) generative adversarial networks (GANs) remote sensing imagery slope loss super-resolution (SR)
ISSN号1939-1404
DOI10.1109/JSTARS.2024.3399544
英文摘要Digital surface model (DSM) is the fundamental data in various geoscience applications, such as city 3-D modeling and urban environment analysis. The freely available DSM often suffers from limited spatial resolution. Super-resolution (SR) is a promising technique to increase the spatial resolution of DSM. However, most existing SR models struggle to reconstruct spatial details, such as buildings, valleys, and ridges. This article proposes a novel DSM super-resolution (DSMSR) model that integrates high-resolution remote sensing imagery using generative adversarial networks. The generator in DSMSR contains three modules. The first DSM feature extraction module uses the residual-in-residual dense block to extract features from low-resolution DSM. The second multiscale attention feature extraction module employs the pyramid convolutional residual dense blocks to capture the spatial details of ground objects at multiple scales from remote sensing imagery. The third DSM reconstruction module uses a squeeze-and-excitation block to fuse the extracted features from low-resolution DSM and high-resolution remote sensing imagery for generating SR DSM. The discriminator of DSMSR uses the relativistic average discriminator for adversarial learning. The slope loss is further introduced to ensure the accurate representation of topographic features. We evaluate DSMSR on four different terrain regions in the U.K. to downscale the 30-m AW3D30 DSM to 5-m DSM. The experimental results indicate that DSMSR outperforms the traditional interpolation algorithms and four existing deep-learning-based SR models. The DSMSR restores more spatial detail of topographic features and generates more accurate image quality, elevation, and terrain metrics.
WOS关键词DEM ; DSM
资助项目National Key Research and Development Program of China
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001246279000019
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/206524]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Chen, Yuehong; Ma, Qiang
作者单位1.China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Disaster Reduct, Beijing 100038, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Hohai Univ, Coll Geog & Remote Sensing, Nanjing 211100, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Guihou,Chen, Yuehong,Huang, Jiamei,et al. Digital Surface Model Super-Resolution by Integrating High-Resolution Remote Sensing Imagery Using Generative Adversarial Networks[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:10636-10647.
APA Sun, Guihou,Chen, Yuehong,Huang, Jiamei,Ma, Qiang,&Ge, Yong.(2024).Digital Surface Model Super-Resolution by Integrating High-Resolution Remote Sensing Imagery Using Generative Adversarial Networks.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,10636-10647.
MLA Sun, Guihou,et al."Digital Surface Model Super-Resolution by Integrating High-Resolution Remote Sensing Imagery Using Generative Adversarial Networks".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):10636-10647.

入库方式： OAI收割

来源：地理科学与资源研究所

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