Special Issue "Remote Sensing for Maritime Safety and Security"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editor

Dr. Raffaella Guida
Website
Guest Editor
Surrey Space Centre, University of Surrey, Guildford, GU2 7XH, UK
Interests: Microwave remote sensing, Synthetic Aperture Radar, Data fusion, Electromagnetic modeling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Maritime safety and security is one of the most fundamental, critical and urgent goals of political agendas, as able to threaten, when at risk, in-country stability and international relationships. An accurate and real-time monitoring of seas and oceans is highly required to provide relevant organizations, governments and agencies with data and tools to support decision-making processes. From multi-sensor satellites to small-sats constellations, from emerging video to more standard optical and SAR data, from data fusion frameworks to single-dataset-based detectors, manifold are the remote sensing technologies, techniques and processing solutions able to support  a safe navigation, the control of maritime traffic and borders, the detection of illegal activities, the prosecution of responsible parties, the marine environment safeguard.

Researchers in the field are invited to contribute to this special issue on Remote Sensing for Maritime Safety and Security with innovative and game-changing remote sensing solutions (in the technologies, techniques or data processing) in any of the maritime applications mentioned above.

.

Dr. Raffaella Guida
Guest Editor

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 papers will be 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 2200 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

  • maritime safety
  • security
  • surveillance
  • remote sensing
  • ship detection and tracking
  • wake detection
  • oil spill detection
  • modeling
  • data fusion

Published Papers (5 papers)

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Research

Open AccessArticle
Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation
Remote Sens. 2019, 11(21), 2506; https://doi.org/10.3390/rs11212506 - 26 Oct 2019
Cited by 3
Abstract
It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds. Many current CNN-based one-stage and two-stage detection methods usually first predefine a series of anchors with various scales, aspect ratios and angles, and then the detection results [...] Read more.
It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds. Many current CNN-based one-stage and two-stage detection methods usually first predefine a series of anchors with various scales, aspect ratios and angles, and then the detection results can be outputted by performing once or twice classification and bounding box regression for predefined anchors. However, most of the defined anchors have relatively low accuracy, and are useless for the following classification and regression. In addition, the preset anchors are not robust to produce good performance for other different detection datasets. To avoid the above problems, in this paper we design a paired semantic segmentation network to generate more accurate rotated anchors with smaller numbers. Specifically, the paired segmentation network predicts four parts (i.e., top-left, bottom-right, top-right, and bottom-left parts) of ships. By combining paired top-left and bottom-right parts (or top-right and bottom-left parts), we can take the minimum bounding box of these two parts as the rotated anchor. This way can be more robust to different ship datasets, and the generated anchors are more accurate and have fewer numbers. Furthermore, to effectively use fine-scale detail information and coarse-scale semantic information, we use the magnified convolutional features to classify and regress the generated rotated anchors. Meanwhile, the horizontal minimum bounding box of the rotated anchor is also used to combine more context information. We compare the proposed algorithm with state-of-the-art object-detection methods for natural images and ship-detection methods, and demonstrate the superiority of our method. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Safety and Security)
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Open AccessArticle
Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness
Remote Sens. 2019, 11(19), 2196; https://doi.org/10.3390/rs11192196 - 20 Sep 2019
Cited by 2
Abstract
The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high [...] Read more.
The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Safety and Security)
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Open AccessArticle
Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction
Remote Sens. 2019, 11(18), 2173; https://doi.org/10.3390/rs11182173 - 18 Sep 2019
Cited by 2
Abstract
To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, [...] Read more.
To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, and then we can predict rotated bounding boxes from rotated region proposals to locate arbitrary-oriented ships more accurately. The two networks share the same deconvolutional layers to perform semantic segmentation for the prediction of center regions and orientations of ships, respectively. They can provide the potential center points of the ships helping to determine the more confident locations of the region proposals, as well as the ship orientation information, which is beneficial to the more reliable predetermination of rotated region proposals. Classification and regression are then performed for the final ship localization. Compared with other typical object detection methods for natural images and ship-detection methods, our method can more accurately detect multiple ships in the high-resolution remote sensing image, irrespective of the ship orientations and a situation in which the ships are docked very closely. Experiments have demonstrated the promising improvement of ship-detection performance. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Safety and Security)
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Open AccessArticle
Oil Spill Segmentation in Ship-Borne Radar Images with an Improved Active Contour Model
Remote Sens. 2019, 11(14), 1698; https://doi.org/10.3390/rs11141698 - 18 Jul 2019
Cited by 2
Abstract
Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film [...] Read more.
Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film detection in ship-borne radar images using pixel area threshold parameters. After applying a pre-processing scheme with a Laplace operator, an Otsu threshold, and mean and median filtering, the shape and area of the oil film can be calculated rapidly. Compared with other ACMs, the improved Local Binary Fitting (LBF) model is robust and has a fast calculation speed for uniform ship-borne radar sea clutter images. The proposed method achieves better results and higher operation efficiency than other automatic and semi-automatic methods for oil film detection in ship-borne radar images. Furthermore, it provides a scientific basis to assess pollution scope and estimate the necessary cleaning materials during oil spills. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Safety and Security)
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Open AccessArticle
A Geometric Barycenter-Based Clutter Suppression Method for Ship Detection in HF Mixed-Mode Surface Wave Radar
Remote Sens. 2019, 11(9), 1141; https://doi.org/10.3390/rs11091141 - 13 May 2019
Abstract
The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter [...] Read more.
The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Safety and Security)
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