电波科学学报2024，Vol.39Issue(6)：1095-1101,7.DOI:10.12265/j.cjors.2023280
基于深度学习的Sentinel-1双极化SAR台风海况下海面风速反演方法
Deep learning-based sea surface wind speed retrieval method for Sentinel-1 dual-polarimetric SAR under typhoon sea state
摘要
Abstract
It is of great significance to realize high-precision observation of the sea surface wind field under typhoon sea conditions for disaster prevention and mitigation.Traditional methods rely on geophysical model functions to retrieve wind speed,which necessitates the input of external wind direction information.The accuracy of wind direction directly affects the accuracy of wind speed retrieval.The deep neural network method combines traditional methods with data mining,and uses this method to retrieve the sea surface wind speed without the input of external wind direction information,which simplifies the retrieval process and opens up a new development direction of sea surface wind speed retrieval for synthetic aperture radar(SAR).However,its fitting ability is limited.To achieve high-precision sea surface wind speed retrieval without the need for external wind direction information input,this paper proposes a deep learning-based method for typhoon sea state and sea surface wind speed retrieval from Sentinel-1 dual-polarimetric SAR.Based on the DenseNet deep learning model,this method achieves high-precision sea surface wind speed retrieval without external wind direction input.Experimental results show that the root mean square error of wind speed inversion by the proposed method can reach 1.741 8 m/s,and the correlation degree can reach more than 0.9,which is better than the inversion results of traditional methods and deep neural network methods.The proposed method further demonstrates the effectiveness of deep learning techniques in the field of SAR sea surface wind field retrieval for sea surface wind field retrieval.关键词
哨兵一号合成孔径雷达(Sentinel-1 SAR)/海面风速反演/深度学习/数据挖掘/台风Key words
Sentinel-1 SAR/sea surface wind speed retrieval/deep learning/data mining/typhoon分类
天文与地球科学引用本文复制引用
李潇寒,闫秋双,范陈清,张杰..基于深度学习的Sentinel-1双极化SAR台风海况下海面风速反演方法[J].电波科学学报,2024,39(6):1095-1101,7.基金项目
国家自然科学基金(61931025) （61931025）
山东省自然科学基金(ZR2021QD010) （ZR2021QD010）
