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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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-08, Vol.13 (16), p.3313
Main Authors: Zhao, Yujin, Zhao, Liaoying, Zhang, Huaguo, Fu, Bin
Format: Article
Language:English
Subjects:
Accuracy
Bathymeters
Bathymetry
Fisheries
Learning algorithms
Long short-term memory
LSTM networks
Machine learning
Model accuracy
Morphology
Neural networks
Remote sensing
Sand
Sand & gravel
sand wave topography
Sand waves
sea surface roughness
sun glitter remote sensing
Surface roughness
Taiwan Banks
Topographic maps
Topographic surveys
Topography
Training
Water depth
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13163313