Description
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.
| Date made available | 8 May 2020 |
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| Publisher | University of Bristol |
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Ship Wake Detection in SAR Images via Sparse Regularization
Karakus, O., Rizaev, I. G. & Achim, A. M., 5 Nov 2019, In: IEEE Transactions on Geoscience and Remote Sensing. 58, 3, p. 1665 - 1677 13 p.Research output: Contribution to journal › Article (Academic Journal) › peer-review
Open AccessFile63 Citations (Scopus)184 Downloads (Pure) -
Ship Wake Detection in X-band SAR Images Using Sparse GMC Regularization
Karakus, O. & Achim, A., 16 Apr 2019, 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), p. 2182-2186 5 p. 8683489. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
16 Citations (Scopus)
Projects
- 1 Finished
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AssenSAR - Assessment of Sea Surface Signatures for Naval Platforms Using SAR Imagery
Achim, A. (Principal Investigator)
1/01/18 → 31/12/21
Project: Research
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