Abstract
In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.
Highlights
A CCURATE characterization of sea surface condition is important in isolation and in the detection and characterization of ship wakes
We propose a novel approach to ship wake detection in synthetic aperture radar (SAR) images, which is based on an inverse problem formulation
Sensitivity quantifies the number of true positive (TP) in proportion to the total number of detections and specificity does the same for true negatives (TNs)
Summary
A CCURATE characterization of sea surface condition is important in isolation and in the detection and characterization of ship wakes. We propose a novel approach to ship wake detection in SAR images, which is based on an inverse problem formulation. The main contribution of this article is to propose an innovative approach based on sparse regularization to obtain the Radon transform of the image, in which the linear features are enhanced. Contrary to [10] and [22], the proposed method detects ship wakes, independently of real information about the ships and the environment, by selecting the required parameters directly from the observed SAR images. Since we model ship wakes as linear features, the SAR image formation can be expressed in terms of its Radon transform as.
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