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Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 19160

Special Issue Editors

Prof. Dr. Alin Achim

E-Mail Website
Guest Editor
Department of Electrical & Electronic Engineering, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol BS8 1UB, UK
Interests: Synthetic Aperture Radar (SAR); computational imaging; inverse problems; statistical signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Oktay Karakus

E-Mail Website
Guest Editor
School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, 5 The Parade, Roath, Cardiff CF24 3AA, UK
Interests: statistical signal/image processing; bayesian data analysis; remote sensing; synthetic aperture radar imaging; inverse problems; convex/non-convex optimisation; Markov chain Monte Carlo methods; nonlinear time series modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate characterization of sea surface conditions is not only important in isolation but also in the detection and characterization of ship wakes. These provide key information for tracking vessels and are also useful in classifying the characteristics of wake-generating vessels. Until recently, one of the main factors hampering research into sea surface modelling was the lack of data of sufficiently high resolution (pixels need to be typically smaller than few meters) and accuracy. Remote-sensing technologies have, however, shown remarkable progress in recent years, and the availability of remotely sensed data of the Earth’s and the sea surfaces is continuously growing. Several European missions (e.g., the Italian COSMO/SkyMed; the German TerraSAR-X; or, more recently, the UK NovaSAR) have developed a new generation of satellites exploiting synthetic aperture radar (SAR) to provide spatial resolutions previously unavailable from space-borne remote sensing. This represents a milestone for ocean-monitoring capabilities but also requires the development of novel image modelling, analysis, and processing techniques, that are able to cope with this new generation of data and to optimally exploit them for information-extraction purposes.

This Special Issue intends to publish both high-quality review papers on existing methodologies for the characterization, simulation, and analysis of SAR images of the sea surface, as well as original research contributions describing new developments of such methodologies. Contributing authors are encouraged to address issues related to the following topics (non-exclusively) in the context of SAR remote sensing of the sea and ocean’s surface:

  • Hydrodynamical modelling of the sea surface and SAR image formation;
  • Statistical modelling of SAR images of the sea surface;
  • Methods for simulating SAR images of the sea surface;
  • Inverse problems in SAR imaging of the sea surface: autofocussing, despeckling, and super-resolution;
  • Machine learning for the analysis of the sea surface;
  • Ship detection in SAR imagery;
  • Ship-wake detection and quantification;
  • Fusion of information from SAR images and from sensors and data sources non-peculiar to remote sensing (e.g., automatic identification system (AIS), meteorological, etc.).
Prof. Alin Achim
Dr. Oktay Karakus
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SAR remote sensing
  • Seas and oceans’ surface
  • Hydrodynamical modelling
  • Ship-wake characterization
  • Computational approaches to modelling and simulations
  • Inverse problems
  • Machine learning
  • Ship detection and identification
  • Automated knowledge extraction
  • Image analysis and data fusion

Published Papers (6 papers)

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24 pages, 1628 KiB  
Article
Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing
by Zi-Yao Zhang, Odysseas Pappas, Igor G. Rizaev and Alin Achim
Remote Sens. 2022, 14(9), 2190; https://doi.org/10.3390/rs14092190 - 04 May 2022
Cited by 2 | Viewed by 1723
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)
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21 pages, 7336 KiB  
Article
DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images
by Kun Sun, Yi Liang, Xiaorui Ma, Yuanyuan Huai and Mengdao Xing
Remote Sens. 2021, 13(14), 2743; https://doi.org/10.3390/rs13142743 - 13 Jul 2021
Cited by 21 | Viewed by 2996
Abstract
Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied [...] Read more.
Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs). Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)
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18 pages, 6420 KiB  
Article
On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
by Pan Xu, Qingyang Li, Bo Zhang, Fan Wu, Ke Zhao, Xin Du, Cankun Yang and Ruofei Zhong
Remote Sens. 2021, 13(10), 1995; https://doi.org/10.3390/rs13101995 - 19 May 2021
Cited by 58 | Viewed by 5181
Abstract
Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis [...] Read more.
Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)
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23 pages, 7100 KiB  
Article
A Method of Marine Moving Targets Detection in Multi-Channel ScanSAR System
by Junying Yang, Xiaolan Qiu, Mingyang Shang, Lihua Zhong and Chibiao Ding
Remote Sens. 2020, 12(22), 3792; https://doi.org/10.3390/rs12223792 - 18 Nov 2020
Cited by 6 | Viewed by 1934
Abstract
Azimuth multi-channel Synthetic Aperture Radar (SAR) system operated in burst mode makes high-resolution ultrawide-swath (HRUS) imaging become a reality. This kind of imaging mode has excellent application value for the maritime scenarios requiring wide-area monitoring. This paper suggests a moving target detection (MTD) [...] Read more.
Azimuth multi-channel Synthetic Aperture Radar (SAR) system operated in burst mode makes high-resolution ultrawide-swath (HRUS) imaging become a reality. This kind of imaging mode has excellent application value for the maritime scenarios requiring wide-area monitoring. This paper suggests a moving target detection (MTD) method of marine scenes based on sparse recovery, which integrates detection, velocity estimation, and relocation. Firstly, the typical phenomenon of scene folding in the coarse-focused domain is introduced in detail. Given that the spatial distribution of moving vessels is highly sparse, the idea of sparse recovery is utilized to acquire the azimuth time characterizing the position of the moving target reasonably. Subsequently, the radial velocity and position information about the targets are obtained simultaneously. What makes the proposed method effective are two characteristics of the moving targets in ocean scenes, high signal-to-clutter ratio (SCR) and sparsity of the spatial distribution. Then, estimation performances under different SCR are analyzed by Monte Carlo experiments. And the actual SCR of the vessels in the ocean scene obtained by GaoFen-3 dual-receive channel mode is invoked as a reference value to verify the effectiveness. Besides, some simulation experiments demonstrate the capability to indicate marine moving targets. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)
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16 pages, 8703 KiB  
Technical Note
Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images
by Maria Daniela Graziano and Alfredo Renga
Remote Sens. 2021, 13(10), 1955; https://doi.org/10.3390/rs13101955 - 17 May 2021
Cited by 11 | Viewed by 2065
Abstract
The recognition of wakes generated by dark vessels is a tremendous and interesting challenge in the field of maritime surveillance by Synthetic Aperture Radar (SAR) images. The paper aims at assessing the detection performance in different scenarios by processing Sentinel-1 SAR images along [...] Read more.
The recognition of wakes generated by dark vessels is a tremendous and interesting challenge in the field of maritime surveillance by Synthetic Aperture Radar (SAR) images. The paper aims at assessing the detection performance in different scenarios by processing Sentinel-1 SAR images along with ground truth data. Results confirm that the Radon-based approach is an effective technique for wake-based detection of dark vessels, and they lead to a deeper understanding of the effects of different sea and wind conditions. In general, the best applicative scenario is a marine image characterized by homogeneous sea clutter; the presence of natural surface film or strong transition from low wind speed areas to more windy zones worsen the detection performance. Nonetheless, the proposed approach features dark vessel detection capabilities by identifying their wakes, without any a priori knowledge of their positions. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)
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14 pages, 6115 KiB  
Technical Note
Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images
by Maria Daniela Graziano
Remote Sens. 2020, 12(18), 2869; https://doi.org/10.3390/rs12182869 - 04 Sep 2020
Cited by 15 | Viewed by 2934
Abstract
Recently, international agencies for border security ask for an improvement of the actual Maritime Situational Awareness. This manuscript presents preliminary results of a detection technique of go-fast boats, whose utilization in illegal affairs is strongly increasing. Their detection is very challenging since: (i) [...] Read more.
Recently, international agencies for border security ask for an improvement of the actual Maritime Situational Awareness. This manuscript presents preliminary results of a detection technique of go-fast boats, whose utilization in illegal affairs is strongly increasing. Their detection is very challenging since: (i) their echo is not visible in SAR images, and (ii) the illegal activities are carried out in the nighttime making useless the optical sensors. However, their wakes are very persistent and extent in SAR images for some kilometers. Hence, the manuscript shows an innovative deterministic methodology for the ship detection based on the wake signature. It firstly identifies pixels crossed by the wakes, whose presence is, then, validated in two steps. The first level of validation estimated how prominent the wake components are with respect to their background. The second level of validation exploits the presence of the wakes among neighbor pixels. The approach has been applied on ships imaged by TerraSAR-X mission showing the same peculiarities of go-fast boats. Results highlight the potentialities of the proposed approach, which can be also conceived as a subsequent step in a hybrid system, whose preliminary wake detection screening is carried out by different techniques. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)
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