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This code implements a method for detecting ship wakes in synthetic aperture radar (SAR) images of the sea surface. The method is based on a linear model assumption for the wakes and hence the Radon transform is employed, within an inverse problem formulation, for detecting the wakes. The cost function associated with the image formation model includes a sparsity enforcing penalty, i.e., the generalized minimax concave (GMC) function. Despite being a nonconvex function, the GMC penalty allows the overall cost function to remain convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using a maximum a posteriori (MAP) estimation.

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... For each image, ship wake structures are classified into three categories: (1) Turbulent Wake, (2) narrow-V Wake (2 arms) and (3) Kelvin wake (2 arms) [22], and the detection performance metrics are calculated depending on correct detection/discard of these wakes. The ship wake detection procedure was implemented by using the AssenSAR Wake Detector Matlab software [23] available as open-source, and for the DT-CWT implementation we used the sample functions published in [24]. The detailed performance analysis for all images is shown in Table II and Table III with the best results in bold. ...
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In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. This is achieved by promoting linear features in the images. For the inverse problem-solving stage, we propose a penalty function, which combines the dual-tree complex wavelet transform (DT-CWT) with the non-convex Cauchy penalty function. The solution to this inverse problem is based on the forward-backward (FB) splitting algorithm to obtain enhanced images in the Radon domain. The proposed method achieves the best results and leads to significant improvement in terms of various performance metrics, compared to state-of-the-art ship wake detection methods. The accuracy of detecting ship wakes in SAR images with different frequency bands and spatial resolution reaches more than 90%, which clearly demonstrates an accuracy gain of 7% compared to the second-best approach.
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