Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network

Huang, Weimin; Yang, Zhiding; Chen, Xinwei

doi:10.1109/jstars.2021.3124969

Cited by 22 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that machine learning algorithms have been applied to H s estimation, which can simplify the cumbersome steps of previous algorithms and improve computational efficiency. It is also possible to estimate more accurate results using methods including a support vector regression (SVR)-based method [24], artificial neural network (ANN)-based methods [25,26], a convolutional neural network (CNN)-based method [27], a convolutional gated recurrent unit network (CGRU)-based method [14], or a temporal convolutional network (TCN)-based method [28]. In addition, random forest (RF)-based machine learning methods have been used to estimate wave directions and periods [29].…”

Section: Introductionmentioning

confidence: 99%

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

Zuo,

Wang,

Wang

et al. 2024

JMSE

View full text Add to dashboard Cite

In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (Hs) and wave period (Ts) from X-band marine radar images. Since the shore-based radar image in this study is interfered with by other radar radial noise lines and solid target objects, to ensure that the proposed convolutional neural network (CNN) extracts the image features accurately, it is necessary to pre-process the radar image to eliminate interference. Firstly, a pre-trained GoogLeNet is used to extract multi-scale depth space features from the radar images to estimate Hs and Ts. Since CNN-based models cannot analyze the temporal behavior of wave features in radar image sequences, self-attention is connected after the deep convolutional layer of the CNN to construct a convolutional self-attention (CNNSA)-based model that generates spatial-temporal features for Hs and Ts estimation. Simultaneously, Hs and Ts measured by nearby buoys are used for model training and reference. The experimental results show that the proposed CNNSA model reduces the RMSD by 0.24 m and 0.11 m, respectively, in Hs estimation compared to the traditional SNR-based and CNN-based methods. In Ts estimation, the RMSD is reduced by 0.3 s and 0.08 s, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

Zuo,

Wang,

Wang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…[ 11 ] proposed a model based on temporal convolutional networks (TCN). The authors achieved a correlation coefficient of 0.90 (without averaging), and an RMSE of 0.32 m by utilizing SNR and EEMD (ensemble empirical mode decomposition) features as inputs for the temporal convolutional networks.…”

Section: Introductionmentioning

confidence: 99%

“…There have been several studies utilizing neural networks to estimate wave heights with Xband radar images. [9] used a set of synthesized X-band radar images to train a convolutional neural network trained (CNN), achieving an RMSE of 0.39 m. [10] estimated significant wave heights using a multilayer perceptron with three features obtained from X-band radar images as input variables, achieving an RMSE of 0.22 m. [11] proposed a model based on temporal convolutional networks (TCN). The authors achieved a correlation coefficient of 0.90 (without averaging), and an RMSE of 0.32 m by utilizing SNR and EEMD (ensemble empirical mode decomposition) features as inputs for the temporal convolutional networks.…”

Section: Introductionmentioning

confidence: 99%

Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions

Kwon,

Chang,

Yang

2023

PLoS ONE

View full text Add to dashboard Cite

This research introduces a deep learning method for ocean wave height estimation utilizing a Convolutional Neural Network (CNN) based on the VGGNet. The model is trained on a dataset comprising buoy wave heights and radar images, both critical for marine engineering. The dataset features X-band radar images sourced from Sokcho, Republic of Korea, spanning from June 1, 2021, to August 13, 2021. This collection amounts to 72,180 three-dimensional images, gathered at intervals of approximately 1.43 seconds. The data collected was highly unbalanced in terms of wave heights, with images of lower wave heights being more common. To deal with data imbalances in the wave height datasets, we categorized the data into three groups based on wave heights and applied stratified random sampling at each level. This approach balances the data patches for each training iteration, reducing the risk of overfitting and promoting learning from diverse data. We also implemented a system to protect data in groups with fewer instances, ensuring fair representation across all categories. This study presents a deep learning regression model for predicting wave height values from radar images. The model extracts features from sequences of 64 radar images using three-dimensional convolutions for both temporal and spatial learning. Using three-dimensional convolutions, the model captures temporal features in radar image sequences and provides accurate wave height estimates with an RMSE of 0.3576 m. The study derived results using radar images under different wave height conditions for 74 days to ensure reliability.

show abstract

“…Instead of using the ideal linear relation, an SVR-based algorithm is used to retrieve the SWH based on the extracted SNR [10,11]. Since the correlation between the SWH and the SNR is not completely linear in practice, methods based on an artificial neural network are presented for enhancing the retrieving accuracy of the SWH [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Method of Extracting the SWH Based on a Constituted Wave Slope Feature Vector (WSFV) from X-Band Marine Radar Images

Wei,

Wang,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m.

show abstract

Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network

Cited by 22 publications

References 34 publications

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions

A Method of Extracting the SWH Based on a Constituted Wave Slope Feature Vector (WSFV) from X-Band Marine Radar Images

Contact Info

Product

Resources

About