首页|An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

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Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method's applicability in retrieving SIR from SAR images.

2-D Cauchy continuous wavelet transform(CWT)Adaboost Regressionsea icesea ice surface roughness

Pengyi Chen、Zhongbiao Chen、Runxia Sun、Yijun He

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School of Marine Sciences,Nanjing University of Information Science&Technology,Nanjing 210044,China

East Sea Information Center of State Oceanic Administration,Shanghai 200136,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Youth Foundation of ChinaNatural Science Youth Foundation of Jiangsu Province under contrantScience and Technology Project of China Huaneng Group Co.,Ltd

2021YFC28033014197730241506199BK20150905HNKJ20-H66

2024

10.1007/s13131-023-2248-9

海洋学报(英文版)
中国海洋学会

海洋学报(英文版)

CSTPCD
影响因子：0.323
ISSN：0253-505X
年,卷(期)：2024.43(5)
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