Remote sensing image computing – Visual Information Laboratory

Example images including ship wakes and their Radon transform. (Upper Left) – Image includes all 5 ship wakes. (Upper Right) – Image does not include Kelvin wake. (Lower row) – Radon transforms of the images above. Both images are TerraSAR-X Stripmap products with HH polarisation and 3m by 3m resolution.

Ship wake detection is essential for extracting information on the wake generating vessels in order to analyse synthetic aperture radar (SAR) images of the sea surface. 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 project, ship wake detection is posed as an inverse problem, which the associated cost function has taken various forms, such as

** Generalized minimax concave (GMC) function [1,2],
** Non-convex Cauchy-based penalty function [3,4],
** Hybrid penalty function which combines the Cauchy-based penalty with a 2-D DWT based term [5].

Despite having a non-convex regularizer, we provide mathematically consistent solutions, by either

** keeping the overall cost function to be convex [1,2], OR
** setting model parameters to guarantee the convergence [3-5].

To quantify the performance of the proposed methods, 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, L_p, nuclear and total variation (TV) norms.

Algorithm

Example Results

Further information

[1] Karakuş, O., & Achim, A. (2019, May). Ship wake detection in X-band SAR images using sparse GMC regularization. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2182-2186). IEEE.

[2] Karakuş, O., Rizaev, I., & Achim, A. (2019). Ship Wake Detection in SAR Images via Sparse Regularization. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 1665-1677.

[3] Yang, T., Karakuş, O., & Achim, A. (2020, October). Detection of ship wakes in SAR imagery using Cauchy regularisation. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 3473-3477). IEEE.

[4] Karakuş, O., & Achim, A. (2020). On solving SAR imaging inverse problems using nonconvex regularization with a Cauchy-based penalty. IEEE Transactions on Geoscience and Remote Sensing.

[5] Ma, W., Achim, A., & Karakuş, O. (2020). Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery. arXiv preprint arXiv:2012.06663.

Contact

Oktay Karakuş, Tianqi Yang, Wanli Ma, Igor Rizaev, Alin Achim

Code

O Karakus, and A Achim. (2020): “AssenSAR Wake Detector.”
https://doi.org/10.5523/bris.f2q4t5pqlix62sv5ntvq51yjy

Funding

This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/R009260/1 (AssenSAR).

In this project, we investigate solutions to a number of inverse problems encountered in SAR imaging. We propose a convex proximal splitting method for the optimization of a cost function that includes a non-convex Cauchy-based penalty. The convergence of the overall cost function optimization is ensured through the careful selection of model parameters within a forward-backwards (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including

** Super-resolution,
** Image formation,
** Despeckling,
** Ship wake detection for maritime applications.

The proposed method is compared to several methods employing classical penalty functions such as total variation (TV) and L₁ norms, and to the generalized minimax-concave (GMC) penalty. We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this paper.