空间科学学报2025，Vol.45Issue(2)：424-436,13.DOI:10.11728/cjss2025.02.2024-0158
基于多特征输入深度学习的浅海水下地形SAR卫星遥感
Shallow-water Bathymetry Mapping from Satellite SAR Imagery Using Deep Learning with Multiple Feature Inputs
摘要
Abstract
Inresponsetothedemandforhigh-precisionshallowseatopographyinversionandto im-prove the limitations of optical remote sensing,this study proposes a deep-learning model utilizing multi-ple feature inputs to inverse shallow sea topography from spaceborne Synthetic Aperture Radar(SAR)images.For the data acquisition and dataset construction,six high-resolution Sentinel-1 dual-polariza-tion SAR images covering the waters northeast of Hainan Island of China in 2024 under different phases and sea conditions were collected,among which six images were used for model training,while the rest were used for testing.The reference depth was obtained from ETOPO.In dataset creation,SAR images are segmented into 8×8 sub-images.The model input is designed to consist of 8 feature variables,which involve the VV polarization backscattering coefficientσVV0,radar incidence angle,geography informa-θ tion(latitude and longitude),and marine dynamic environmental parameters.The model output is refer-ence depth from ETOPO with spatial consistency.The deep learning network comprises a convolutional layer,two BottleNeck modules from ResNet,and a fully connected layer.The final model performances in retrieving shallow water depth are shown as follows:For the training set,the model achieved a Root Mean Square Error(RMSE)of 1.57 m,and the average absolute percentage error is 6.56%,with the maximum detectable water depth reaching 49.05 m.The model presented with an RMSE of 1.95 m,and the average absolute percentage error is 11.55%for the testing dataset.Additionally,there is little differ-ence between the two scenes with different temporal and sea conditions,indicating that the model is sta-ble and robust.Thus,the proposed model was based on the brightness patterns observed in SAR im-agery,which can detect shallow-water depths up to 50 m with high precision.关键词
合成孔径雷达/浅海地形反演/深度学习/多特征输入Key words
Synthetic Aperture Radar(SAR)/Shallow sea bathymetry/Deep learning/Multiple feature inputs分类
海洋科学引用本文复制引用
崔宜德,汪胜,于暘,刘桂红,马文韬,黄岩,杨涛,杨晓峰..基于多特征输入深度学习的浅海水下地形SAR卫星遥感[J].空间科学学报,2025,45(2):424-436,13.基金项目
国家重点研发计划项目资助(2023YFB3907700) （2023YFB3907700）
