X-band marine radar is an effective tool for sea wave remote sensing. Conventional physical-based methods for acquiring wave parameters from radar sea clutter images use three-dimensional Fourier transform and spectral analysis. They are limited by some assumptions, empirical formulas and the calibration process while obtaining the modulation transfer function (MTF) and signal-to-noise ratio (SNR). Therefore, further improvement of wave inversion accuracy by using the physical-based method presents a challenge. Inspired by the capability of convolutional neural networks (CNN) in image characteristic processing, a deep-learning inversion method based on deep CNN is proposed. No intermediate step or parameter is needed in the CNN-based method, therefore fewer errors are introduced. Wave parameter inversion models were constructed based on CNN to inverse the wave’s spectral peak period and significant wave height. In the present paper, the numerically simulated X-band radar image data were used for a numerical investigation of wave parameters. Results of the conventional spectral analysis and CNN-based methods were compared and the CNN-based method had a higher accuracy on the same data set. The influence of training strategy on CNN-based inversion models was studied to analyze the dependence of a deep-learning inversion model on training data. Additionally, the effects of target parameters on the inversion accuracy of CNN-based models was also studied.

中文翻译：

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深度卷积神经网络对合成X波段雷达海杂波图像进行遥感的数值研究

X波段船用雷达是海浪遥感的有效工具。从雷达海杂波图像获取波参数的常规基于物理的方法使用三维傅里叶变换和频谱分析。在获得调制传递函数（MTF）和信噪比（SNR）的同时，它们受到一些假设，经验公式和校准过程的限制。因此，通过使用基于物理的方法来进一步提高波反转精度提出了挑战。受到卷积神经网络在图像特征处理中的能力的启发，提出了一种基于深度CNN的深度学习反演方法。基于CNN的方法不需要任何中间步骤或参数，因此引入的错误更少。基于CNN构建了波参数反演模型，以反演波的频谱峰值周期和有效波高。在本文中，将数值模拟的X波段雷达图像数据用于波参数的数值研究。比较了常规光谱分析和基于CNN的方法的结果，并且基于CNN的方法在相同数据集上具有更高的准确性。研究了训练策略对基于CNN的反演模型的影响，以分析深度学习反演模型对训练数据的依赖性。此外，还研究了目标参数对基于CNN的模型反演精度的影响。数值模拟的X波段雷达图像数据被用于波参数的数值研究。比较了常规光谱分析和基于CNN的方法的结果，并且基于CNN的方法在相同数据集上具有更高的准确性。研究了训练策略对基于CNN的反演模型的影响，以分析深度学习反演模型对训练数据的依赖性。此外，还研究了目标参数对基于CNN的模型反演精度的影响。数值模拟的X波段雷达图像数据被用于波参数的数值研究。比较了常规光谱分析和基于CNN的方法的结果，并且基于CNN的方法在相同数据集上具有更高的准确性。研究了训练策略对基于CNN的反演模型的影响，以分析深度学习反演模型对训练数据的依赖性。此外，还研究了目标参数对基于CNN的模型反演精度的影响。