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