3 research outputs found
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
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