Wave Height Estimation from Shipborne X-Band Nautical Radar Images

A shadowing-analysis-based algorithm is modified to estimate significant wave height from shipborne X-band nautical radar images. Shadowed areas are first extracted from the image through edge detection. Smith’s function fit is then applied to illumination ratios to derive the root mean square (RMS) surface slope. From the RMS surface slope and the mean wave period, the significant wave height is estimated. A data quality control process is implemented to exclude rain-contaminated and low-backscatter images. A smoothing scheme is applied to the gray scale intensity histogram of edge pixels to improve the accuracy of the shadow threshold determination. Rather than a single full shadow image, a time sequence of shadow image subareas surrounding the upwind direction is used to calculate the average RMS surface slope. It has been found that the wave height retrieved from the modified algorithm is underestimated under rain and storm conditions and overestimated for cases with low wind speed. The modified method produces promising results by comparing radar-derived wave heights with buoy data, and the RMS difference is found be 0.59 m

Evaluation and improvement of methods for estimating sea surface wave parameters from X-band marine radar data

In this thesis, several algorithms have been proposed for estimating ocean wave parameters from X-band marine radar data, i.e., wave direction, wave period, and significant wave height. In the first part of this study, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a sub-image selection scheme is proposed to mitigate the rain effect. Firstly, each radar image is divided into multiple sub-images, and the sub-images with relatively clear wave signatures are identified based on a random-forest based classiffication model. Then, wave direction is estimated by performing a Radon transform (RT) on each valid sub-image. As for wave period estimation, a random-forest based regression method is proposed. Texture features are first extracted from each pixel of the selected sub-image using the gray-level co-occurrence matrix (GLCM) and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest based wave period regression model. Shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and to validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by 6.9ﾟ, 6.0ﾟ, 4.9ﾟ, and 1.0ﾟ for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13 s, 0.20 s, 0.30 s, and 0.20 s under those four rainfall intensity levels, respectively. The second part of research focuses on the estimation of significant wave height (Hₛ). A temporal convolutional network (TCN)-based model is proposed to retrieve Hₛ from X-band marine radar image sequences. Three types of features are first extracted from radar image sequences based on signal to noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and GLCM methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce Hₛ estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as simultaneously collected wave data from several wave buoys deployed nearby are used for model training and testing. After averaging, experimental results show that the TCN-based model further improves the Hₛ estimation accuracy, with reductions of RMSEs by 0.33 m and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that with the same feature extraction scheme, TCN outperforms other machine-learning based algorithms including support vector regression (SVR) and the convolutional gated recurrent unit (CGRU) network

Sea surface wind and wave parameter estimation from X-band marine radar images with rain detection and mitigation

In this research, the application of X-band marine radar backscatter images for sea surface wind and wave parameter estimation with rain detection and mitigation is investigated. In the presence of rain, the rain echoes in the radar image blur the wave signatures and negatively affect estimation accuracy. Hence, in order to improve estimation accuracy, it is meaningful to detect the presence of those rain echoes and mitigate their influence on estimation results. Since rain alters radar backscatter intensity distribution, features are extracted from the normalized histogram of each radar image. Then, a support vector machine (SVM)-based rain detection model is proposed to classify radar images obtained between rainless and rainy conditions. The classification accuracy shows significant improvement compared to the existing threshold-based method. By further observing images obtained under rainy conditions, it is found that many of them are only partially contaminated by rain echoes. Therefore, in order to segment between rain-contaminated regions and those that are less or unaffected by rain, two types of methods are developed based on unsupervised learning techniques and convolutional neural network (CNN), respectively. Specifically, for the unsupervised learning-based method, texture features are first extracted from each pixel and then trained using a self organizing map (SOM)-based clustering model, which is able to conduct pixel-based identification of rain-contaminated regions. As for the CNN-based method, a SegNet-based semantic segmentation CNN is �rst designed and then trained using images with manually annotated labels. Both shipborne and shore-based marine radar data are used to train and validate the proposed methods and high classification accuracies of around 90% are obtained. Due to the similarities between how haze affects terrestrial images and how rain affects marine radar images, a type of CNN for image dehazing purposes, i.e., DehazeNet, is applied to rain-contaminated regions in radar images for correcting the in uence of rain, which reduces the estimation error of wind direction significantly. Besides, after extracting histogram and texture features from rain-corrected radar images, a support vector regression (SVR)-based model, which achieves high estimation accuracy, is trained for wind speed estimation. Finally, a convolutional gated recurrent unit (CGRU) network is designed and trained for significant wave height (SWH) estimation. As an end-to-end system, the proposed network is able to generate estimation results directly from radar image sequences by extracting multi-scale spatial and temporal features in radar image sequences automatically. Compared to the classic signal-to-noise (SNR)-based method, the CGRU-based model shows significant improvement in both estimation accuracy (under both rainless and rainy conditions) and computational efficiency

Algorithms for wind parameter retrieval from rain-contaminated x-band marine radar images

In this thesis, research for retrieving wind direction and speed from rain-contaminated X-band marine radar images is presented. Firstly, a method for retrieving wind direction from X-band marine radar data is proposed. The algorithm is used to investigate radar backscatter in the wavenumber domain and obtain wind direction from the wavenumber spectrum. For rain-contaminated images collected under low wind speeds (i.e. less than 8 m/s), wind directions are retrieved using spectral components with wavenumbers of [0.01, 0.2] rad/m. For rain-contaminated images obtained under high wind speeds and rain-free images, wind directions are retrieved using the spectral values at wavenumber zero. The algorithm was tested using X-band radar images and anemometer data collected on the east coast of Canada. Comparison with the anemometer data shows that the root mean square error (RMSE) of wind directions retrieved from low-wind-speed rain-contaminated images is reduced by 25.1 ◦ . Secondly, two methods for estimating wind speed from X-band nautical radar images are presented. One method is used to determine wind speeds by relating the spectral strengths of radar backscatter to the wind speeds using a logarithmic function. The other method is used to mitigate rain influence by applying gamma correction to rain-contaminated images, and then relate the average radar image intensities to measured wind speeds with a logarithmic function. Comparison with the anemometer data show that the two methods reduce the RMSEs of wind speeds estimated from rain-contaminated radar data by 5.9 m/s and 5.4 m/s, respectively. Unlike existing methods which require the exclusion of rain-contaminated data, the new wind parameter retrieval methods work well for both rain-contaminated and rain-free images

The theory and applications of ocean wave measuring systems at and below the sea surface, on the land, from aircraft, and from spacecraft

Methods for measuring and analyzing ocean waves are described, including those presently in use on spacecraft and planned for SEASAT-A. Potential difficulties with synthetic aperture systems for a spacecraft are described and an alternate design is suggested. The different methods can yield different kinds of spectra and other kinds of imagery. Ways to compare different kinds of data are given. The scientific and practical applications of data from spacecraft are given

Ocean wind and wave parameter estimation from ship-borne x-band marine radar data

Ocean wind and wave parameters are important for the study of oceanography, on- and off-shore activities, and the safety of ship navigation. Conventionally, such parameters have been measured by in-situ sensors such as anemometers and buoys. During the last three decades, sea surface observation using X-band marine radar has drawn wide attention since marine radars can image both temporal and spatial variations of the sea surface. In this thesis, novel algorithms for wind and wave parameter retrieval from X-band marine radar data are developed and tested using radar, anemometer, and buoy data collected in a sea trial off the east coast of Canada in the North Atlantic Ocean. Rain affects radar backscatter and leads to less reliable wind parameters measurements. In this thesis, algorithms are developed to enable reliable wind parameters measurements under rain conditions. Firstly, wind directions are extracted from raincontaminated radar data using either a 1D or 2D ensemble empirical mode decomposition (EEMD) technique and are seen to compare favourably with an anemometer reference. Secondly, an algorithm based on EEMD and amplitude modulation (AM) analysis to retrieve wind direction and speed from both rain-free and rain-contaminated X-band marine radar images is developed and is shown to be an improvement over an earlier 1D spectral analysis-based method. For wave parameter measurements, an empirical modulation transfer function (MTF) is required for traditional spectral analysis-based techniques. Moreover, the widely used signal-to-noise ratio (SNR)-based method for significant wave height (HS) estimation may not always work well for a ship-borne X-band radar, and it requires external sensors for calibration. In this thesis, two methods are first presented for HS estimation from X-band marine radar data. One is an EEMD-based method, which enables satisfactory HS measurements obtained from a ship-borne radar. The other is a modified shadowingbased method, which enables HS measurements without the inclusion of external sensors. Furthermore, neither method requires the MTF. Finally, an algorithm based on the Radon transform is proposed to estimate wave direction and periods from X-band marine radar images with satisfactory results

A Novel Scheme for Extracting Sea Surface Wind Information From Rain-Contaminated X-Band Marine Radar Images

The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this article, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self-organizing map (SOM)-based clustering model. Then, a convolutional neural network used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly

Estimation of swell height using spaceborne GNSS-R data from eight CYGNSS satellites

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.This work was supported in part by the National Natural Science Foundation of China under Grant 42174022, in part by the Future Scientists Program of China University of Mining and Technology under Grant 2020WLKXJ049, in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_2003, in part by the Programme of Introducing Talents of Discipline to Universities, Plan 111, Grant No. B20046, and in part by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202106420009).Peer ReviewedPostprint (published version

Study on Real-Time Ocean Wave Analysis Based on X-Band Radar Measurement Data

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2023. 2. 김용환.해양 활동의 안전성 및 효율을 향상하기 위해 신뢰도 높은 파랑 정보의 획득이 요구됨에 따라 전 세계적으로 다양한 방식의 파랑 계측이 수행되고 있다. 이 중 해양 X-band 레이더는 넓은 영역의 파랑 정보를 동시에 계측할 수 있으므로 단시간의 계측을 통해 통계적으로 수렴도 높은 해양파 정보를 얻을 수 있다는 장점이 있다. 때문에, 다양한 선박 및 해양 구조물에서 해양 X-band 레이더를 활용하여 파랑 계측을 수행하고 있으며, 계측 기법의 고도화에 대한 다양한 논의가 진행되고 있다. 해양 레이더는 안테나에서 송신된 X-band 마이크로파와 해면상 잔물결 간의 Bragg 공진 현상에 의해 후방 산란되는 전자기파의 세기를 계측한다. 이러한 원격 계측 과정은 그림자, 기울임, 유체동역학적 효과 등 수많은 비물리적 변조 효과를 수반한다. 따라서, 레이더 이미지로부터 파랑 정보를 도출하기 위해서는 이미지 강도에 포함된 비물리적 성분을 제거하고 스펙트럼의 에너지를 유의파고에 따라 조정하는 파랑장 재구성 과정이 요구된다. 본 논문은 고도화된 파랑장 재구성 기법에 대한 연구를 다루고 있다. 제시된 전체 재구성 절차는 그림자 기반 유의파고 추정과 3D-FFT 기반 파랑장 재구성으로 구성되며, 각 해석 과정이 높은 연산 효율을 지니고 있다. 또한, 본 연구에서는 파랑장 재구성 기법의 정확도 향상을 위해 그림자 발생의 공간 통계적 특성을 엄밀하게 고려하였다. 이를 위해, 유의파고 추정 시 해면의 공간상 자기상관함수 및 평균표면경사의 직교성을 고려하였고, 파랑장 재구성 시 그림자 발생의 공간적 특성에 기인하는 불균일한 분산 분포에 대한 보정을 수행하였다. 본 논문에서 제시된 기법의 검증을 위해 합성 레이더 이미지와 실해역 레이더 이미지에 대해 파랑장 재구성을 수행하였다. 먼저, 다양한 해상 상태의 합성 레이더 이미지를 생성하여 해석에 활용하였고, 해상 상태에 따른 재구성 정확도의 의존성을 살펴보았다. 이를 통해, 다양한 해상 상태에서 그림자 효과에 대한 엄밀한 고려를 통해 파랑장 재구성 정확도를 향상할 수 있음을 확인하였다. 다음으로, 이어도 해양과학기지 및 기상 1호에서 계측된 실해역 레이더 이미지에 대한 유의파고 추정을 수행하였다. 그 결과, 실해역 데이터에 대하여 정확도 높은 유의파고 추정이 가능함을 확인하였다.It is required to obtain reliable wave information to improve the safety and efficiency of marine activities. Various methods for wave measurements are being carried out around the world. Among them, the marine X-band radar has the advantage that it can obtain statistically converged wave information based on short-time measurement. This is because the wave radar can simultaneously measure wave elevation data in a large area. Accordingly, marine X-band radars are installed on various ships and marine platforms to perform wave measurements. Diverse discussions on X-band radar-based wave field analysis techniques are also steadily underway. In general, incoherent marine radar measures the backscattered intensity due to Bragg scattering between X-band microwaves transmitted from the antenna and ripples on the sea surface. This remote sensing process entails numerous non-physical modulation effects, such as shadowing, tilting, and hydrodynamic effects. Therefore, a series of post-processing called wave-field reconstruction is required to retrieve wave information from marine radar images. The wave-field reconstruction procedure consists of removing the non-physical components from the measured spectrum, and adjusting the total spectral energy according to the significant wave height (HS). In this study, the advanced wave-field reconstruction technique is presented. The overall reconstruction procedure is comprised of the shadowing-based HS estimation and 3D-FFT-based wave-field reconstruction, and both of each analysis process have high computational efficiency. Thats why it is suitable for real-time wave-field analysis. To enhance the wave analysis, the statistical characteristics of the shadowing effect were rigorously considered. For this purpose, the spatial autocorrelation function of the ocean surface and the orthogonality of the mean surface slope were considered for HS estimation. Moreover, the uneven variance distribution owing to the spatial dependency of the shadowing effect was mitigated during the wave-field reconstruction. Wave-field reconstruction was applied to the synthetic and real radar images to verify the presented technique. The HS estimation and 3D-FFT-based wave-field reconstruction were performed for synthetic radar images corresponding to various states, and the dependence of this technique on the sea state was examined. As a result, it was confirmed that the reconstruction accuracy could be improved through the rigorous consideration of stochastic characteristics of the shadowing effect for all cases. Moreover, HS estimation was performed for real radar images collected from the Ieodo ocean research station and RV Gisang 1. In conclusion, a satisfactorily accurate HS estimation was also achieved.1. 서론 1 1.1 연구 배경 1 1.2 기존 연구 3 1.2.1 3D-FFT 기반 파랑장 재구성 3 1.2.2 유의파고 추정 5 1.3 연구 목표 및 주요 연구 내용 7 2. 파랑장 재구성 9 2.1 위상 분해 파랑장 재구성 문제 9 2.1.1 문제 정의 9 2.1.2 전체 해석 절차 11 2.2 유의파고 추정 13 2.2.1 그림자 영역 구분 13 2.2.2 Smith 함수 기반 표면 경사 추정 14 2.2.3 총표면경사 추정 18 2.2.4 유의파고 계산 19 2.3 3D-FFT 기반 파랑장 재구성 21 2.3.1 Mean-shift 변형 21 2.3.2 에너지 분포 보정 21 2.3.3 3차원 고속 푸리에 변환(3D-FFT) 23 2.3.4 필터링 23 2.3.5 변조 전달 함수(MTF) 24 3. 합성 레이더 이미지 해석 26 3.1 합성 레이더 이미지 생성 26 3.2 유의파고 추정 30 3.2.1 평균표면경사 추정 30 3.2.2 스펙트럼 해석 34 3.2.3 유의파고 추정 결과 36 3.3 파랑장 재구성 38 3.3.1 에너지 분포 보정 38 3.3.2 재구성 결과 40 4. 실해역 레이더 이미지 해석 45 4.1 데이터셋 정의 45 4.1.1 이어도 데이터셋 45 4.1.2 NIMS 데이터셋 46 4.2 유의파고 추정 결과 48 4.2.1 이어도 데이터셋 해석 결과 48 4.2.2 NIMS 데이터셋 해석 결과 53 5. 결론 57 6. 공학적 기여 및 향후 연구 59 참고문헌 61 부록 66 A.1 Smith 함수 유도 66 A.2 해면 평균표면경사의 물리적 특성 71석