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 large, shallow sand wave areas. Remote sensing inversion methods to detect Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a remote model based long short-term memory network machine learning. Using multi-angle sun glitter obtain sea surface roughness (SSR) information learning training SSR its corresponding information, region extracted. The accuracy validated through application 774 km2 area Banks. obtains root mean square error 3.31–3.67 m, indicating that method good generalization capability can achieve understanding waves with some Sand widely present tidal environments; our low requirements for ground data, value.