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Publication - Professor Alin Achim

    Ship Wake Detection in SAR Images via Sparse Regularisation

    Citation

    Karakuş, O, Rizaev, I & Achim, A, 2019, ‘Ship Wake Detection in SAR Images via Sparse Regularisation’. arXiv.

    Abstract

    In order to analyse SAR images of the sea surface, detection of ship wakes is very important since they carry essential information about vessels. Due to wakes' linear characteristics, ship wake detection approaches are based on transforms such as Radon and Hough, which express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is addressed as an inverse problem employing the inverse Radon transform. The associated cost function includes a sparsity enforcing penalty, the generalized minimax concave (GMC) function as prior. Despite being a non-convex regularizer, the GMC penalty enforces the 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. In order to quantify the performance of the proposed method, various types of SAR images are utilised 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 prior 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. Our results show that our proposed technique offers the best performance by achieving 80% success rate.

    Full details in the University publications repository