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

Sea state from monoscopic ocean video in real environments

Video of the ocean surface is used as a means for estimating useful information about the scene. A methodology is first introduced for approximating the pixel to metre scale from high-scale videos of the ocean, such as from an aeroplane. Radar images are used for testing. The temporal and spatial domains are associated through the phase modulation of waves, and a process is introduced that selects the waves with the highest energy to be used for estimating the pixel scale. The spatial information is then used with the calculated pixel scale for approximating the sea state. Due to the difficulty of obtaining high-scale videos, a methodology is then introduced that uses the temporal variation from video, and specifically time series of pixel intensities. It aims to isolate and utilise the temporal variation of the wave field from all other video elements, such as environmental brightness fluctuations. The methodology utilises the Kalman filter and the least squares approximate solution for providing an uncalibrated video amplitude spectrum. A method is proposed for scaling this spectrum to metres with the use of an empirical model of the ocean. The significant wave height is estimated from the calibrated video amplitude spectrum. Videos of the ocean in real environments from a shipborne camera and a tower are used for testing. In both sets of data, in situ buoy information is used solely for validation. The next technique aims to approximate the sea state from the same kind of data, namely videos of the ocean in real environments, without calibrating a video amplitude spectrum. The proposed methodology tracks the principal component of the movement of water in the video, which is speculated to be associated with the dominant frequency of the ocean. To accomplish this, the singular spectrum analysis algorithm and the extended Kalman filter are used. Then, the shape of an empirical spectrum is utilised in order to translate the dominant frequency output into a significant wave height estimation. The problem of not using ocean theory associated with a particular empirical energy spectrum for calibration is examined in the next methodology. A secondary oscillatory component from the singular spectrum analysis algorithm is identified with the incorporation of the extended Kalman filter. Ocean theory involving the equilibrium range of oceans is used for calibration. The shipborne videos are used for testing the behaviour of the techniques for approximately the same sea state of 3.1m to 3.4m of significant wave height. The tower videos are used for testing the techniques for a variety of sea states ranging between 0.5m and 3.6m of significant wave height. From all methodologies, the maximum observed values of root mean square error 0.37m and of mean absolute percentage error 18% suggest that the work is promising at estimating these states

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

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

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

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

Vessel collision threat detection for offshore oil and gas installations

There is a potential for major structural damage to offshore installations leading to fatalities and serious injuries in the event of collision by either a passing or an in-field seagoing vessel. Both categories of collision have occurred on the UK Continental Shelf (UKCS) although to date only significant, rather than catastrophic, consequences have occurred. Internationally, collisions have occurred that have caused both loss of life and environmental damage. This report considers collision threat detection and updates Research Report RR514 (2006). RR1154 considers the Ship/Platform Collision Incident Database which was previously described in Research Report RR053 (2001). Collision threat detection via radar and visual watch keeping is one of the major duties that the Emergency Response and Rescue Vessel (ERRV) crew needs to conduct for monitoring and appraisal of risks to UKCS installations. Detection tools are subject to a number of limitations and this report investigates technological advancements including: (1) deployment of automated radar detection and tracking devices to supplement the work of ERRV crews and assist in the overall collision risk management strategy; and (2) the implementation of Automatic Identification System (AIS) equipment in the global marine regulatory system which has also had an impact on vessel identification and the processes through which an errant vessel can be warned off. Results are discussed in terms of both how they may affect current operations and how they may be adopted in future to enhance offshore safety

Earth resources: A continuing bibliography with indexes (issue 58)

This bibliography lists 500 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure