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Article

LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks

1
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
*
Author to whom correspondence should be addressed.
Academic Editors: Fenzhen Su, Cunjin Xue and Han Xiao
Remote Sens. 2021, 13(16), 3313; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163313
Received: 8 June 2021 / Revised: 17 August 2021 / Accepted: 18 August 2021 / Published: 21 August 2021
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value. View Full-Text
Keywords: Taiwan Banks; sand wave topography; LSTM networks; machine learning; sun glitter remote sensing; sea surface roughness Taiwan Banks; sand wave topography; LSTM networks; machine learning; sun glitter remote sensing; sea surface roughness
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MDPI and ACS Style

Zhao, Y.; Zhao, L.; Zhang, H.; Fu, B. LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks. Remote Sens. 2021, 13, 3313. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163313

AMA Style

Zhao Y, Zhao L, Zhang H, Fu B. LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks. Remote Sensing. 2021; 13(16):3313. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163313

Chicago/Turabian Style

Zhao, Yujin, Liaoying Zhao, Huaguo Zhang, and Bin Fu. 2021. "LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks" Remote Sensing 13, no. 16: 3313. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163313

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