标题: An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

作者: Zhou, AN (Zhou, Annan); Chen, YM (Chen, Yumin); Wilson, JP (Wilson, John P.); Su, H (Su, Heng); Xiong, ZX (Xiong, Zhexin); Cheng, QS (Cheng, Qishan)

来源出版物: REMOTE SENSING 卷: 13 期: 16 文献号: 3089 DOI: 10.3390/rs13163089 出版年: AUG 2021

摘要: High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.

入藏号: WOS:000690123400001

语言: English

文献类型: Article

作者关键词: convolutional neural networks; DEMs; super-resolution; deep learning

地址: [Zhou, Annan; Chen, Yumin; Su, Heng; Xiong, Zhexin; Cheng, Qishan] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wilson, John P.] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA 90089 USA.

通讯作者地址: Chen, YM (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: 2016301110183@whu.edu.cn; ymchen@whu.edu.cn; jpwilson@usc.edu; 2017301110076@whu.edu.cn; 2016301110034@whu.edu.cn; 2016301110176@whu.edu.cn

影响因子：4.848