李潇寒,闫秋双,范陈清,等. 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,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2023280
      引用本文: 李潇寒,闫秋双,范陈清,等. 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,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2023280
      LI X H, YAN Q S, FAN C Q, et al. Deep learning-based sea surface wind speed retrieval method for Sentinel-1 dual-polarimetric SAR under typhoon sea state[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2023280
      Citation: LI X H, YAN Q S, FAN C Q, et al. Deep learning-based sea surface wind speed retrieval method for Sentinel-1 dual-polarimetric SAR under typhoon sea state[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). 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: 实现台风海况下海面风场的高精度观测,对防灾减灾等具有重要意义。传统方法使用地球物理模型函数反演风速,需要外部风向信息的输入,风向的精度将直接影响风速反演的精度。深度神经网络方法将传统方法与数据挖掘相融合,使用此方法反演海面风速,不需要外部风向信息的输入,简化了反演过程,开拓了合成孔径雷达(synthetic aperture radar, SAR)海面风速反演的新发展方向,但其拟合能力有限。为实现高精度且无需外部风向信息输入的海面风速反演,本文提出了一种基于DenseNet深度学习模型的Sentinel-1双极化SAR台风海况下海面风速反演方法。实验结果表明,本文方法反演风速的均方根差可达到1.7418 m/s,相关度可达0.9以上,优于传统方法和深度神经网络方法的反演结果。本文提出的方法进一步证明了深度学习技术在SAR海面风场反演领域的有效性可为海面风场反演提供新思路,开辟新方向。

         

        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 1.7418 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.

         

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