Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network

2020 ◽  
Vol 76 (6) ◽  
pp. 465-477
Author(s):  
Sihan Xue ◽  
Xupu Geng ◽  
Xiao-Hai Yan ◽  
Ting Xie ◽  
Qiuze Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Sar Imagery

Validation of significant wave height retrieval from co-polarization Chinese Gaofen-3 SAR imagery using an improved algorithm

2018 ◽  
Vol 37 (6) ◽  
pp. 1-10 ◽  
Author(s):  
Yexin Sheng ◽  
Weizeng Shao ◽  
Shuai Zhu ◽  
Jian Sun ◽  
Xinzhe Yuan ◽  
...  
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Sar Imagery ◽  
Improved Algorithm

The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach

2004 ◽  
Vol 126 (3) ◽  
pp. 213-219 ◽  
Author(s):  
Felice Arena ◽  
Silvia Puca
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Error Function ◽  
Neural Network Approach ◽  
Significant Wave ◽  
Time Periods ◽  
Weighted Error ◽  
Storm Model

A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of modelled data. The algorithm uses a weighted error function during the learning phase, to improve the modelling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangular storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods.

Correlation Enhanced Machine Learning Approach based Wave Height Prediction

2018 ◽  
Vol 4 (5) ◽  
pp. 10
Author(s):  
Ruchi Shrivastava ◽  
Dr. Krishna Teerth Chaturvedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Work Performance ◽  
Feature Reduction ◽  
Learning Approaches ◽  
Significant Wave ◽  
Wave Parameters ◽  
Height Prediction

The prediction of wave height is one of the major problems of coastal engineering and coastal structures. In recent years, advances in the prediction of significant wave height have been considerably developed using flexible calculation techniques. In addition to the traditional prediction of significant wave height, soft computing has explored a new way of predicting significant wave heights. This research was conducted in the direction of forecasting a significant wave height using machine learning approaches. In this paper, a problem of significant wave height prediction problem has been tackled by using wave parameters such as wave spectral density. This prediction of significant wave height helps in wave energy converters as well as in ship navigation system. This research will optimize wave parameters for a fast and efficient wave height prediction. For this Pearson’s, Kendall’s and Spearman’s Correlation Coefficients and Particle Swarm Optimization feature reduction techniques are used. So reduced features are taken into consideration for prediction of wave height using neural network. In this work, performance evaluation metrics such as MSE and RMSE values are decreased and gives better performance of classification that is compared with existing research’s implemented methodology. From the experimental results, it is observed that proposed algorithm gives the better prediction as compared to PSO feature reduction technique. So, it is also concluded that Co-relation enhanced neural network is better as compared to PSO based neural network with increased number of features.

scholarly journals Data-Based Modelling of Significant Wave Height in the Adriatic Sea

2017 ◽  
Vol 6 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Luka Mudronja ◽  
Petar Matić ◽  
Marko Katalinić
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wave Height ◽  
Significant Wave Height ◽  
Adriatic Sea ◽  
Single Point ◽  
Flexible Tool ◽  
Significant Wave ◽  
Artificial Neural ◽  
Input Variables

The paper deals with sea wave modelling based on available data acquired from a satellite-calibrated numerical model. The idea is to use an artificial neural network, as a flexible tool capable of modelling nonlinear processes, for significant wave height (SWH) modelling at a single point in the Adriatic Sea. The focus of the paper was not to develop a new type of ANN, but rather to use it as a modelling tool and identify the most significant input variables for SWH modelling in the Adriatic Sea, among the available data. Linear and nonlinear regression models were also developed for purposes of comparison of neural network performances with those of traditional data modelling methods. A total of 22 years of data were used - 20 years of data with a 6 h sampling step time, i.e. 30684 data samples were used to calibrate the models, while 2 years of data, i.e. 2920 data samples were used to test the models’ performances. Simulation results proved the ability of an artificial neural network to model SWH with high ccuracy based on available data. Furthermore, the artificial neural network model proved to be more accurate than traditional statistical models, especially when multiple input variables were used.

scholarly journals An Empirical Algorithm to Retrieve Significant Wave Height from Sentinel-1 Synthetic Aperture Radar Imagery Collected under Cyclonic Conditions

2018 ◽  
Vol 10 (9) ◽  
pp. 1367 ◽  
Author(s):  
Weizeng Shao ◽  
Yuyi Hu ◽  
Jingsong Yang ◽  
Ferdinando Nunziata ◽  
Jian Sun ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Significant Wave Height ◽  
Synthetic Aperture ◽  
Training Dataset ◽  
Sar Images ◽  
Data Set ◽  
Significant Wave ◽  
Sar Imagery ◽  
Aperture Radar

In this study, an empirical algorithm is proposed to retrieve significant wave height (SWH) from dual-polarization Sentinel-1 (S-1) synthetic aperture radar (SAR) imagery collected under cyclonic conditions. The retrieval scheme is based on the well-known CWAVE empirical function that is here updated to deal with multi-polarization S-1 SAR measurements collected using the interferometric wide (IW) and the Extra Wide-Swath (EW) imaging modes, under cyclonic conditions. First, a training dataset that consists of six S-1 SAR images collected under cyclonic conditions is exploited to both tune the retrieval function and to check the soundness of the retrievals against the co-located WAVEWATCH-III (WW3) numerical simulations. The comparison of simulation from the WW3 model and measurements from altimeter Jason-2 shows a 0.29m root mean square error (RMSE) of significant wave height (SWH). Then, a testing data-set that consists of two S-1 SAR images is exploited to provide a preliminary validation. The results, verified against both WW3 and European Centre for Medium-Range Weather Forecasts (ECMWF) data, show the soundness of the herein approach.

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