Ship Wake Detection in SAR Images via Sparse Regularization

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.Comment: 18 page

A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface

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.Comment: 6 pages, 2 Figures. This work has been presented in IGARSS 202

МЕТОД ФИЛЬТРАЦИИ ЦИФРОВЫХ МОДЕЛЕЙ РАСТИТЕЛЬНОГО ПОКРОВА НА ОСНОВЕ ЛАЗЕРНОГО СКАНИРОВАНИЯ

In this paper, we propose a new approach to remove erroneous data during creating canopy height models based on the laser scanning technology in GIS environ-ment. The approach automatically removes the artifacts and contains the only one variable parameter - a value of the artifact’s relative depth. In general, our results show that proposed method efficiently (>90%) removes artifacts within the tree crowns, with maximum efficiency of 98,4%.В работе рассматривается новый метод удаления ошибочных данных при создании цифровых моделей растительного покрова на основе технологии лазерного сканирования в среде ГИС. Метод автоматически удаляет артефакты и содержит только один изменяемый параметр - величину относительной глубины артефактов. Результаты показали, что разработанный метод в целом эффективно (>90%) удаляет артефакты в пределах крон деревьев, а максимальный процент удаленных артефактов составляет 98,4%

Modeling and SAR imaging of the sea surface: A review of the state-of-the-art with simulations

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 Kelvin 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 and the example of a real SAR image. The study gives a fuller understanding of radar imaging mechanisms for sea waves and ship wakes

Modeling and SAR Imaging of the Sea Surface:a Review of the State-of-the-Art with Simulations

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. 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 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, signal polarization, mean incidence angle, image resolution, 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

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilising a sparsity-enforcing Cauchy regularizer is proposed, and an alternating minimization framework is used to solve it, in which the desired image and the phase errors are estimated alternatively. For the image reconstruction sub-problem (f-sub-problem), two methods are presented that are capable of handling the problem’s complex nature. Firstly, we design a complex version of the forward-backward splitting algorithm to solve the f-sub-problem iteratively, leading to a complex forward-backward autofocusing method (CFBA). For the second variant, techniques of Wirtinger calculus are utilized to minimize the cost function involving complex variables in the f-sub-problem in a direct fashion, leading to Wirtinger alternating minimization autofocusing (WAMA) method. For both methods, the phase error estimation sub-problem is solved by simply expanding and observing its cost function. Moreover, the convergence of both algorithms is discussed in detail. Experiments are conducted on both simulated and real SAR images. In addition to the synthetic scene employed, the other SAR images focus on the sea surface, with two being real images with ship targets, and another two being simulations of the sea surface (one of them containing ship wakes). The proposed method is demonstrated to give impressive autofocusing results on these datasets compared to state-of-the-art methods

Wave power resource dynamics for the period 1980-2021 in Atlantic Europe's Northwest seas

This paper explores the variability of wave power in space and time using a 42-year high-resolution hindcast wave model from the Copernicus Marine Environment Monitoring Service for the North-West European shelf. We calculate the wave energy flux using significant wave height and wave energy period. To improve wave power assessment, we use knowledge about mean wavelengths and bathymetry, which is necessary given the nature of the intermediate and shallow waters in the region. The results provide monthly, seasonal, and inter-annual estimates of wave power variability based on 122,728 modeled wave measurements. The study advances the understanding of wave energy resources within the domain