Estimating significant wave height from SAR imagery based on an SVM regression model

Gao, Dong; Liu, Yongxin; Meng, Junmin; Jia, Yongjun; Fan, Chenqing

doi:10.1007/s13131-018-1203-7

Cited by 20 publications

(8 citation statements)

References 20 publications

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“…where hIi present the average value of the SAR intensity imagery. In order to evaluate the sensitivity of Cvar on swell and wind wave height, the Elfouhaily spectrum is suggested to simulate the wind wave and swell wave spectrum and further explore the correlation between Cvar of corresponding simulated SAR imagery and sea state [32]. Figure 6 shows the variation of swell wave and wind wave height with an Clearly, a quasilinear curve between the simulated swell wave height and Cvar is found, which means that Cvar can be performed as an imagery characterization for swell information.…”

Section: Influence Of Swell Onmentioning

confidence: 99%

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Wang

Jiao

2021

Journal of Sensors

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The main work of this paper is to explore the influence of swell wave on retrieval of wind speed using ENVISAT ASAR wave mode imagery. The normalized radar cross section (NRCS) scene under different sea states is simulated to investigate the relationship between NRCS variation with swell height, together with swell direction. Moreover, the key parameter of imagery variance (Cvar) is selected to describe the swell wave on SAR imagery. In addition, the imagery parameters of skewness and kurtosis are together analyzed as a function of collocated significant swell wave height and wind speed. Based on the analyzed results, a new method for wind speed retrieval is proposed using ENVISAT ASAR, namely, F(n). Besides the CMOD parameters of NRCS, incidence angle, and relative wind direction, the imagery parameters of Cvar, skewness, and kurtosis are used to compensate for the influence of swell wave on wind speed retrieval in F(n). Finally, the collocated European Centre for Medium-Range Weather Forecast (ECMWF) wind speed dataset and ENVISAT ASAR wave mode imagery are used to verify the retrieval precision and compare with CMOD functions. It is concluded that the F(n) model performs much better than other CMOD functions, with a correlation of 0.89, a bias of 0.08, a RMSE of 1.2 m/s, and an SI of 0.1.

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Section: Influence Of Swell Onmentioning

confidence: 99%

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Wang

Jiao

2021

Journal of Sensors

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“…With the development of artificial intelligence, machine learning methods such as Artificial Neural Networks, Random Forests, and Support Vector Machine are gradually applied to SWH prediction work (Peres et al, 2015;Deshmukh et al, 2016;Gopinath and Dwarakish, 2016;Berbic ́et al, 2017;Gao et al, 2018;Callens et al, 2020). Compared with traditional methods, this kind of method has the advantages of generality and fast calculation speed.…”

Section: Introductionmentioning

confidence: 99%

Significant wave height prediction based on deep learning in the South China Sea

Hao

Gao

2023

Front. Mar. Sci.

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Significant wave height (SWH) prediction can effectively improve the safety of marine activities and reduce the occurrence of maritime accidents, which is of great significance to national security and the development of the marine economy. In this study, we comprehensively analyzed the SWH prediction performance of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) by considering different input lengths, prediction lengths, and model complexity. The experimental results show that (1) the input length impacts the prediction results of SWH, but it does not mean that the longer the input length, the better the prediction performance. When the input length is 24h, the prediction performance of RNN, LSTM, and GRU models is better. (2) The prediction length influences the SWH prediction results. As the prediction length increases, the prediction performance gradually decreases. Among them, RNN is not suitable for 48h long-term SWH prediction. (3) The more layers of the model, the better the SWH prediction performance is not necessarily. When the number of layers is set to 3 or 4, the model’s prediction performance is better.

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“…Evdokia et al [29] predicted sea-state in an offshore wind farm based on machine learning techniques. Gao et al [31] established the SWH retrieval model based on the support vector machine in an ASAR WM.…”

Section: Introductionmentioning

confidence: 99%

Wave Height Estimation and Validation Based on the UFS Mode Data of Gaofen-3 in South China Sea

Cui

2022

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

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The ultrafine strip (UFS) of the Gaofen-3 (GF-3) satellite provides extensive ocean wave details due to the high spatial resolution of 3 m. In this article, a new empirical model is developed to estimate wave height from GF-3 UFS mode data in South China Sea (SCS). Traditional methods either have more input parameters or complex forms or complex transformations, and most of them depend on the visible wave pattern. The model has the advantages of fewer input parameters and unnecessary visual wave patterns. The wave height estimation model is developed based on the collated data pairs between GF-3 UFS data in horizontal-horizontal polarization and the ERA5 reanalysis dataset from 2018 to 2021. The datasets are randomly divided into two groups. One group (about 80%) is used to develop the empirical relation; the other group (about 20%) and some altimeter data are used for significant wave height (SWH) verification. The comparison of the model data with the remaining ERA5 (ECMWF Reanalysis v5) data and the altimeter shows that the root-mean-square error is 0.41 and 0.47 m, the scatter index is 29.24% and 29.79%, and the correlation coefficient is 0.90 and 0.82, respectively. These statistical indices suggest that the developed model is suitable for SWH retrievals. So, the study results indicate that the GF-3 UFS mode data provide valuable information about wave conditions in the SCS.

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Estimating significant wave height from SAR imagery based on an SVM regression model

Cited by 20 publications

References 20 publications

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

Significant wave height prediction based on deep learning in the South China Sea

Wave Height Estimation and Validation Based on the UFS Mode Data of Gaofen-3 in South China Sea

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