Publications

13) Exploiting the dual-tree complex wavelet transform for ship wake detection in SAR imagery (Wanli Ma, Alin Achim, Oktay Karakuş) 

Accepted for presentation in ICASSP 2021

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.

 

12) Modeling and SAR Imaging of the Sea Surface: a Review of the State-of-the-Art with Simulations (Igor Rizaev, Oktay Karakuş, S. John Hogan, Alin Achim)

arXiv preprint

Among other remote sensing technologies, synthetic aperture radar (SAR) has become firmly established in the practice of oceanographic research. Despite solid experience in this field, comprehensive knowledge and interpretation of ocean/sea and vessel wave signatures on radar images are still very challenging. This is not only due to the complex mechanisms involved in the SAR imaging of moving waves: Many technical parameters and scanning conditions vary for different SAR platforms, which also imposes some restrictions on the cross-analysis of their respective images. Numerical simulation of SAR images, on the other hand, allows the analysis of many radar imaging parameters including environmental, ship, or platform related. In this paper, we present a universal simulation framework for SAR imagery of the sea surface, which includes the superposition of sea-ship waves. This paper is the first attempt to cover exhaustively all SAR imaging effects for the sea waves and ship wakes scene. The study is based on well proven concepts: the linear theory of sea surface modeling, Michell thin-ship theory for Kelvin wake modeling, and ocean SAR imaging theory. We demonstrate the role of two main factors that affect imaging of both types of waves: (i) SAR parameters and (ii) Hydrodynamic related parameters such as wind state and Froude number. The SAR parameters include frequency (X, C, and L-band), signal polarization (VV, HH), mean incidence angle, image resolution (2.5, 5 and 10 m), variation by scanning platform (airborne or spaceborne) of the range-to-velocity (R/V) ratio, and velocity bunching with associated shifting, smearing and azimuthal cutoff effects. We perform modeling for five wave frequency spectra and four ship models. We also compare spectra in two aspects: with Cox and Munk’s probability density function (PDF), and with a novel proposed evaluation of ship wake detectability. The simulation results agree well with SAR imaging theory. The study gives a fuller understanding of radar imaging mechanisms for sea waves and ship wakes.

 

11) A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling (Oktay Karakuş, Ercan E. Kuruoglu, Alin Achim)

arXiv preprint

In this paper, we present a novel statistical model, the generalized-Gaussian-Rician (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterizing SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed statistical model is based on the Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be generalized-Gaussian distributed. The proposed amplitude GG-Rician model is further extended to cover the intensity SAR signals. In the experimental analysis, the GG-Rician model is investigated for amplitude and intensity SAR images of various frequency bands and scenes in comparison to state-of-the-art statistical models that include K, Weibull, Gamma, and Lognormal. In order to decide on the most suitable model, statistical significance analysis via Kullback-Leibler divergence and Kolmogorov-Smirnov statistics are performed. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes, and its applicability on both amplitude and intensity SAR images.

 

10) On Solving SAR Imaging Inverse Problems Using Non-Convex Regularisation with a Cauchy-based Penalty (Oktay Karakuş, Alin Achim) arXiv link

In IEEE Transactions on Geoscience and Remote Sensing

Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of applications, including climate change, environmental monitoring, meteorology, high dimensional mapping, ship monitoring, or planetary exploration. In this paper, 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 careful selection of model parameters within a forward-backward (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including super-resolution, image formation, and despeckling, as well as 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.

 

9) A Simulation Study to Evaluate the Performance of the Cauchy proximal operator in Despeckling SAR Images of the sea surface (Oktay Karakuş, Igor Rizaev, Alin Achim)

In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2020

Presentation links for (pdf) and (mp4)

The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated.

 

8) Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties (Oktay Karakuş, Perla Mayo, Alin Achim) arXiv link

In IEEE Transactions on Signal Processing

In this paper, we propose a convex proximal splitting methodology with a non-convex penalty function based on the heavy-tailed Cauchy distribution. We first suggest a closed-form expression for calculating the proximal operator of the Cauchy prior, which then makes it applicable in generic proximal splitting algorithms. We further derive the required condition for minimisation problems with the Cauchy based penalty function that guarantees the convergence to the global minimum even though it is non-convex. Setting the system parameters by satisfying the proposed condition keeps the overall cost function convex, which is minimised via the forward-backward (FB) algorithm. The Cauchy regularisation based proposed method is evaluated in solving two generic signal processing examples, which are a 1D signal denoising in the frequency domain and two 2D image reconstruction studies of de-blurring and denoising. We experimentally verify the proposed convexity conditions for various cases, and show the effectiveness of the proposed Cauchy based non-convex penalty function over the state-of-the-art penalty functions of L1 and total variation (TV) norms.

 

7) The Effect of Sea State on Ship Wake Detectability in Simulated SAR Imagery (Igor Rizaev, Oktay Karakuş, Alin Achim)

In the 27th IEEE International Conference on Image Processing (ICIP) 2020

Ship wake detection methods are mostly based on analyzing real SAR images of the sea surface. This is due to SAR imaging having achieved considerable maturity and becoming effective for their visualization, in particular through Bragg resonance scattering. However, in different environmental conditions, it is often difficult, sometimes impossible, to consider all possible factors that can dramatically change ship wake visualization. In this paper, an analysis of one important sea state factor, namely the fetch length, both for airborne and satellite SAR platforms is investigated and its contribution to the visualization of ship wakes in simulated SAR images is quantified. We study the effect of fetch in terms of wake detectability using a state-of the-art method. The sea surface modelling is performed using the Joint North Sea Wave Project (JONSWAP) spectrum, whilst for Kelvin wake modelling the Michell theory is employed. The simulation results performed help clarify the influence of the sea state on ship wake visualization in SAR imagery.

 

6) Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation (Tianqi Yang, Oktay Karakuş, Alin Achim)

In the 27th IEEE International Conference on Image Processing (ICIP) 2020

arXiv link and Presentation links for (pdf) and (mp4)

Ship wake detection is of great importance in the characterisation of synthetic aperture radar (SAR) images of the ocean surface since wakes usually carry essential information about vessels. Most detection methods exploit the linear characteristics of the ship wakes and transform the lines in the spatial domain into bright or dark points in a transform domain, such as the Radon or Hough transforms. This paper proposes an innovative ship wake detection method based on sparse regularisation to obtain the Radon transform of the SAR image, in which the linear features are enhanced. The corresponding cost function utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution. The detection accuracy of the Cauchy prior based approach is 86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.

 

5) Modelling Sea Clutter in SAR Images Using Laplace-Rician Distribution (Oktay Karakuş, Ercan E. Kuruoglu, Alin Achim)

In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Presentation links for (pdf) and (mp4)

This paper presents a novel statistical model for the characterisation of synthetic aperture radar (SAR) images of the sea surface. The analysis of ocean surface is widely performed using satellite imagery as it produces information for wide areas under various weather conditions. An accurate SAR amplitude distribution model enables better results in despeckling, ship detection/tracking and so forth. In this paper, we develop a new statistical model, namely the Laplace-Rician distribution for modelling amplitude SAR images of the sea surface. The proposed statistical model is based on Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be Laplace distributed. The Laplace-Rician model is investigated for SAR images of the sea surface from COSMO-SkyMed and Sentinel-1 in comparison to state-of-the-art statistical models such as K, lognormal and Weibull distributions. In order to decide on the most suitable model, statistical significance analysis via Kullback-Leibler divergence and Kolmogorov-Smirnov statistics is performed. The results show a superior modelling performance of the proposed model for all of the utilised images.

 

4) A Modification of Rician Distribution for SAR Image Modelling (Oktay Karakuş, Ercan E. Kuruoglu, Alin Achim)

Accepted for presentation in EUSAR 2021

This paper presents a novel statistical model i.e. the Laplace-Rician distribution, for the characterisation of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterising SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed Laplace-Rician model is investigated for SAR images of several frequency bands and various scenes in comparison to state-of-the-art statistical models that include K, Weibull, and Lognormal. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes.

 

3) Ship Wake Detection in SAR Images via Sparse Regularisation (Oktay Karakuş, Igor Rizaev, Alin Achim) arXiv link

In IEEE Transactions of Geoscience and Remote Sensing

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.

 

2) Ship Wake Detection in X-band SAR Images Using Sparse GMC Regularization (Oktay Karakuş, Alin Achim)

In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Ship wakes have crucial importance in the analysis of SAR images of the sea surface due to the information they carry about vessels. Since ship wakes mostly appear as lines in SAR images, line detection methods have been widely used for their identification. In the literature, common practice for detecting ship wakes is to use Hough and Radon transforms in which bright (dark) lines appear as peaks (troughs) points. In this paper, the ship wake detection problem is addressed as a Radon transform based inverse problem with a sparse non-convex generalized minimax concave (GMC) regularization. Despite being a non-convex regularizer, the GMC penalty enforces the cost function to be convex. The solution to this convex cost function optimisation is obtained in a Bayesian formulation and the lines are recovered as maximum a posteriori (MAP) point estimates with a sparse GMC based prior. The detection procedure consists of a restricted area search in the Radon domain and the validation of candidate wakes. The performance of the proposed method is demonstrated in TerraSAR-X images of five different ships and with a total of 19 visible ship wakes. The results show a successful detection performance of up to 84% for the utilised images.

 

1) Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery (Odysseas Pappas, Alin Achim, David Bull)

In IEEE Geoscience and Remote Sensing Letters

Synthetic aperture radar (SAR) is one of the most widely employed remote sensing modalities for large-scale monitoring of maritime activity. Ship detection in SAR images is a challenging task due to inherent speckle, discernible sea clutter, and the little exploitable shape information the targets present. Constant false alarm rate (CFAR) detectors, utilizing various sea clutter statistical models and thresholding schemes, are near ubiquitous in the literature. Very few of the proposed CFAR variants deviate from the classical CFAR topology; this letter proposes a modified topology, utilizing superpixels (SPs) in lieu of rectangular sliding windows to define CFAR guardbands and background. The aim is to achieve better target exclusion from the background band and reduced false detections. The performance of this modified SP-CFAR algorithm is demonstrated on TerraSAR-X and SENTINEL-1 images, achieving superior results in comparison to classical CFAR for various background distributions.