Abstract
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.
Highlights
Sea wave remote sensing has important scientific significance and practical value [1]
Conventional wave inversion methods are limited by assumptions involved in the calibration process
Inspired by the capability of convolutional neural networks (CNN) techniques in handling image problems, a machine learning inversion method based on CNN was proposed
Summary
Sea wave remote sensing has important scientific significance and practical value [1]. The conventional spectral analysis method based on a three-dimensional Fourier transform is an effective approach for extracting wave parameters from radar images [6,7,8,9]. To address deficiencies of the conventional method, and in view of the advantages of CNN in image processing, we developed a CNN-based technique for wave parameter inversion from radar sea clutter images. In this problem, the synthetic radar images are the inputs and the outputs are values of spectral peak period and significant wave height.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have