Quad-polarimetric SAR sea state retrieval algorithm from Chinese Gaofen-3 wave mode imagettes via deep learning

Wang, He; Yang, Jianguo; Lin, Mingsen; Li, Weiwei; Zhu, Jianrong; Ren, Lin; Cui, Lianhua

doi:10.1016/j.rse.2022.112969

Cited by 33 publications

(28 citation statements)

References 53 publications

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“…With the evolution of deep learning in image processing, models based on deep learning have emerged as effective tools for retrieving wave parameters from SAR features. It should be noted recent advancements consider two-dimensional image-level features containing energy distribution information for significant wave height retrieval [31], [32].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

Fang,

Fan,

Cao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic Aperture Radar (SAR) is widely used for observing sea surfaces and retrieving two-dimensional wave spectra. However, existing methods for retrieving directional wave spectra from SAR imagettes face challenges due to the complex non-linear SAR-wave imaging relationship and the limitation of first-guess spectra. This study proposes a novel twostage machine learning strategy for retrieving two-dimensional directional wave spectra from Chinese Gaofen-3 SAR wave mode products. We achieve the generation of complete two-dimensional wave spectra and several wave parameters solely from GF-3 SAR data without necessitating any additional inputs. In the first stage, we employ the Energy Attention Conditional Generative Adversarial Network (EA-CGAN) to retrieve the normalized wave spectrum. The generator of the EA-CGAN establishes a non-linear transformation from normalized SAR cross spectra to normalized wave spectra to enhance the capabilities. In the second stage, the XGBoost model retrieves the intensity of the wave spectrum. The EA-CGAN and XGBoost models were trained on an extensive dataset that consists of about 11,000 Gaofen-3 SAR wave mode imagettes and two-dimensional wave spectra from the fifth-generation reanalysis (ERA-5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results of the evaluation using test samples reveal high consistency between the retrieved wave spectra and ERA-5 wave spectra in terms of spectral similarity, peak period, peak direction, significant wave height, and mean wave periods. Compared to the traditional methods, our approach offers enhanced effectiveness, demonstrating the potential of advanced deep learning in highprecision SAR wave spectrum inversion.

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Section: Introductionmentioning

confidence: 99%

A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

Fang,

Fan,

Cao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In addition, various models were developed from the dependence of SWH on λ c , on its own or combined with one or several of the variables such as NRCS, cvar, skewness, kurtosis, peak wavelength, peak wave direction, and incidence angle (θ) [16], [18]- [24]. Recently, several machine learning and deep learning models have been developed to predict SWH from SAR using some or all of the features mentioned above [24]- [29]. The early investigations were primarily focused on predicting SWH solely from singlepolarization SAR (mostly VV) [14]- [16], [18]- [21], [24], [27]- [28].…”

Section: Introductionmentioning

confidence: 99%

“…Then several studies showed that cross-polarization (HV and VH) information can help to improve SWH retrieval performance [30]. Recent studies have shifted their focus towards exploring the enhancement of combination of multiple polarizations [17], [22]- [23], [25]- [26], [29]. The SAR SWH retrieval entails a multitude of features, and the inclusion of multi-polarization information leads to a multiplication in their number.…”

Section: Introductionmentioning

confidence: 99%

Optimized Estimation of Azimuth Cutoff for Retrieval of Significant Wave Height and Wind Speed From Polarimetric Gaofen-3 SAR Wave Mode Data

Zheng,

Yan,

Fan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study presents an innovative approach for estimating the azimuth cut-off wavelength (λc) using a multipolarization combination technique to enhance the retrieval of significant wave height (SWH) and wind speed (WS) from Gaofen-3 (GF-3) SAR wave mode data. The study identifies distinct advantages of co-polarization for low to moderate sea states and cross-polarization for high sea states in the λc estimation. Consequently, a suite of dual and quad-polarization combination methods is proposed, with the VV+VH combination demonstrating superior cost-efficiency, reducing the Root Mean Square Error (RMSE) of λc estimation by around 20% compared to VV polarization. Correlation analysis between λc at various polarizations, particularly VV+VH, and factors such as SWH, WS, wind direction, wave direction, and incidence angle, indicates a strong positive relationship with SWH and WS, and a moderate relationship with incidence angle. This insight informs the development of three λc-based SWH and WS retrieval models: Single Linear Regression (SLR), Multiple Linear Regression (MLR), and Gaussian Process Regression (GPR). The MLR and GPR models integrate Normalized Radar Cross Section (NRCS) and incidence angle to improve retrieval accuracy. The GPR model achieves more accurate estimation of SWH and WS compared to existing λc-based algorithms, with an RMSE of 0.485 m for SWH retrieval and 1.390 m/s for WS retrieval. Despite the performance gap with state-of-the-art algorithms, the GPR model offers exceptional cost-effectiveness and surpasses NRCS-based models for WS retrieval without requiring wind direction input.

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“…These algorithms include a semiparametric retrieval algorithm (SPRA) [23], partition rescaling and shift algorithm [24], parameterized first-guess spectrum method (PFSM) [25], and fully polarimetric technique [26,27]. Additionally, empirical models [28,29] and machine learning techniques [30,31] have been implemented for retrieving wave parameters from SAR images directly, without calculating the complex model transfer functions (MTFs) of the mapping mechanism.…”

Section: Introductionmentioning

confidence: 99%

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Leng,

Hao,

Shao

et al. 2024

Remote Sensing

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This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.

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Quad-polarimetric SAR sea state retrieval algorithm from Chinese Gaofen-3 wave mode imagettes via deep learning

Cited by 33 publications

References 53 publications

A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

Optimized Estimation of Azimuth Cutoff for Retrieval of Significant Wave Height and Wind Speed From Polarimetric Gaofen-3 SAR Wave Mode Data

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

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