Obtaining accurate bathymetric maps is very valuable for marine environment monitoring,port planning,and so on.Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters chall...Obtaining accurate bathymetric maps is very valuable for marine environment monitoring,port planning,and so on.Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency,but it is limited by the quantity,quality,and water quality of samples.This study introduces a fast feature cascade learning model(FFCLM)to enhance the accuracy of bathymetric inversion from multispectral satellite images,particularly when limited field samples are available.FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting.Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth,satellite,and in situ data.Comparative analysis with conventional machine learning algorithms,including support vector machine,random forest,and gradient boosting trees,indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas.This is especially more pronounced when using small training samples(n<100).Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion.This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters,utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.展开更多
基金supported by the 2023 Hainan Province“South China Sea New Star”Science and Technology Innovation Talent Platform Project(NHXXRCXM202316)in part by Hainan Natural Science Foundation of China(nos.424QN253 and 620RC602)+5 种基金by the National Natural Science Foundation of China(no.61966013)in part by the Teaching Reform Research Project,Hainan Normal University,hsjg2023-07in part by the National Natural Science Foundation of China under grant 61991454in part by the National Key Research and Development Program of China under grant 2023Y FC3107605in part by the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under grant SL2022ZD206in part by the Scientific Research Fund of Second Institute of Oceanography,MNR under grant SL2302.
文摘Obtaining accurate bathymetric maps is very valuable for marine environment monitoring,port planning,and so on.Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency,but it is limited by the quantity,quality,and water quality of samples.This study introduces a fast feature cascade learning model(FFCLM)to enhance the accuracy of bathymetric inversion from multispectral satellite images,particularly when limited field samples are available.FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting.Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth,satellite,and in situ data.Comparative analysis with conventional machine learning algorithms,including support vector machine,random forest,and gradient boosting trees,indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas.This is especially more pronounced when using small training samples(n<100).Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion.This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters,utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.
