LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks
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...
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doaj-4f4935537e534200a01269bf28eafddd2021-08-26T14:18:00ZengMDPI AGRemote Sensing2072-42922021-08-01133313331310.3390/rs13163313LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan BanksYujin Zhao0Liaoying Zhao1Huaguo Zhang2Bin Fu3School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaShallow 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 km<sup>2</sup> 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.https://www.mdpi.com/2072-4292/13/16/3313Taiwan Bankssand wave topographyLSTM networksmachine learningsun glitter remote sensingsea surface roughness |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yujin Zhao Liaoying Zhao Huaguo Zhang Bin Fu |
spellingShingle |
Yujin Zhao Liaoying Zhao Huaguo Zhang Bin Fu LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks Remote Sensing Taiwan Banks sand wave topography LSTM networks machine learning sun glitter remote sensing sea surface roughness |
author_facet |
Yujin Zhao Liaoying Zhao Huaguo Zhang Bin Fu |
author_sort |
Yujin Zhao |
title |
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks |
title_short |
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks |
title_full |
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks |
title_fullStr |
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks |
title_full_unstemmed |
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks |
title_sort |
lstm-based remote sensing inversion of largescale sand wave topography of the taiwan banks |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-08-01 |
description |
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 km<sup>2</sup> 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. |
topic |
Taiwan Banks sand wave topography LSTM networks machine learning sun glitter remote sensing sea surface roughness |
url |
https://www.mdpi.com/2072-4292/13/16/3313 |
work_keys_str_mv |
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