Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea

Snapir, Boris; Waine, Toby; Biermann, Lauren

doi:10.3390/rs11030353

Cited by 34 publications

(28 citation statements)

References 24 publications

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“…Coastal countries around the world are experiencing significant economic losses from illegal, unreported, and unregulated (IUU) fishing activities, and are engaged in a variety of activities to regulate and patrol them [37,38]. The visible embedded imaging radar water (VIIRS) vessel detection method for fishing and other vessel recognition off the coast of India has introduced an algorithm that complements AIS data with vessel monitoring systems (VMS).…”

Section: Natural Resources Managementmentioning

confidence: 99%

“…This algorithm can improve accuracy with machine learning. In order to monitor illegal fishing in the North Sea, a high-resolution commercial synthetic aperture radar (SAR) image and AIS data are compared using the random forest (RF) algorithm, one of the machine learning techniques [38].…”

Section: Natural Resources Managementmentioning

confidence: 99%

“…Since not all ships are AIS data-and VMS (vessel monitoring system)-compliant, they also use supplementary information such as synthetic aperture radar (SAR) in satellite remote sensing (RS) for vessel recognition [31,38]. High-frequency surface-wave radar (HFSWR) for monitoring maritime crimes in the Gulf of Guinea's economic exclusive zone (EEZ) also showed the use of land and satellite ANIS data to compensate for technical shortcomings that increase the need for a low signal [47].…”

Section: Affordabilitymentioning

confidence: 99%

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The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Lee

Mokashi

Moon

et al. 2019

JMSE

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The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.IMO has been leading in and enforcing the use of AIS in ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. U.S. federal agencies actively access nationwide AIS (NAIS) information to perform a variety of functions, including security, safety, and policy-making.However, access to the data needed by non-federal stakeholders, including in the marine industry and academic fields, is still limited; AIS data are not very well known, and related research and use is at the preliminary stage. However, if the importance and usefulness of AIS information were known, stakeholders could make rapid progress in the use of the data and its related applications in the near future. Therefore, this study aims to help non-federal stakeholders understand the AIS data and support future research by assessing difficulties and improving access to data and its applications.This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. It examines the existing literature relating to AIS data and its derived products of application. The study is structured as follows: first, this study discusses the background and introduces the status of the data in Section 2. Data scientists and information engineers understand the need for data and determine the right information to collect. Because data consumes memory and storage space, it can be expensive depending on the size of the data. This is discussed in Section 3, on the nature of the data and the policy of collecting, using, and sharing data. Views of data science, information management, and governance are described in Section 4. This section describes the methods and tools for collecting, storing, distributing, and visualizing AIS data. Section 5 explores the evolution and development of applications using AIS. Finally, in the concluding section, the research is summarized, with suggestions to increase the accessibility and utilization of AIS data.

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Section: Natural Resources Managementmentioning

confidence: 99%

Section: Natural Resources Managementmentioning

confidence: 99%

Section: Affordabilitymentioning

confidence: 99%

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The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Lee

Mokashi

Moon

et al. 2019

JMSE

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“…However, the optical remote sensing image cannot detect the ship at night. While the SAR image has the characteristics of allweather and all-day imaging, the method has large data volume and high cost, which makes it lack of universal ship recognition capabilities in large-area sea areas [4,5]. Therefore, there are still many limitations in the monitoring of marine vessels, especially the lack of monitoring means for marine vessels at night.…”

Section: Introductionmentioning

confidence: 99%

Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

et al. 2020

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Nighttime light remote sensing images show significant application potential in marine ship monitoring, but in areas where ships are densely distributed, the detection accuracy of the current methods is still limited. This article considered the LJ1-01 data as an example, compared with the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data, and explored the application of high-resolution nighttime light images in marine ship detection. The radiation values of the aforementioned two images were corrected to achieve consistency, and the interference light sources of the ship light were filtered. Then, when the threshold segmentation and two-parameter constant false alarm rate methods are combined, the ships’ location information was with obtained, and the reliability of the results was analyzed. The results show that the LJ1-01 data can not only record more potential ship light but also distinguish the ship light and background noise in the data. The detection accuracy of the LJ1-01 data in both ship detection methods is significantly higher than that of the NPP/VIIRS data. This study analyzes the characteristics, performance, and application potential of the high-resolution nighttime light data in the detection of marine vessels. The relevant results can provide a reference for the high-precision monitoring of nighttime marine ships.

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“…In ref. [2] fishing vessels detection is addressed with reference to the problem of global overfishing. A method is here developed to deal with the discrimination between fishing and non-fishing vessels and a showcase is presented.…”

mentioning

confidence: 99%

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

2019

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Remote sensing is a powerful tool used to obtain an unprecedented amount of information about the ocean from a distance, usually from satellites or aircrafts. Measurements collected by active and passive remote sensing instruments can be used for both marine and maritime applications. They allow monitoring of vast areas of the Earth that are difficult to access and sample using traditional methods. Within this context, the observation of targets at sea, e.g.; man-made targets (ships or oil/gas rigs/platforms and wind turbines) and natural targets (icebergs, surfactants, etc.) is nowadays a very hot-topic in the field of global monitoring of environment and security.Among the remote sensing tools, Synthetic Aperture Radar (SAR) is one of the most used since it allows all-day and almost all-weather observations with a moderate-to-fine spatial resolution. SAR imagery gives the possibility to overcome the limits of maritime patrol allowing non-cooperative all-day target surveillance, over wide regions and under almost all-weather conditions. An increasing number of SAR satellites have become available since the early 1990s. This unprecedented development in SAR sensors requires the definition of new techniques and algorithms to detect marine targets, as well as in the assessment of existing methods. Hence, although there is a great deal of literature that concerns SAR methods for detecting targets at sea, there is still room to improve both models and methods.Within this context, methods and models are proposed in this Special Issue (SI) to exploit single-and multi-polarization SAR measurements for observation of targets at sea, with special interest for vessels.In ref.[1] the complex coherence between the two channels of the dual-polarized Sentinel-1 SAR imagery is exploited to detect ships in a timely manner using the entire image. The proposed rationale is verified against real SAR imagery that includes a large number of vessels of different sizes. In addition, Automatic Identification System (AIS) data are used for an extensive and large-scale cross-comparison with the SAR-based detections. Experimental results clearly point out the remarkable detection rate of the SAR-based method and its complementarity with respect to AIS information.In ref.[2] fishing vessels detection is addressed with reference to the problem of global overfishing. A method is here developed to deal with the discrimination between fishing and non-fishing vessels and a showcase is presented. The method, which is based on the Random Forest (RF) classifier, takes as input the vessel's length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the AIS and Sentinel-1 SAR imagery that refers to the North Sea.In ref.[3] a system to monitor illegal fishing activities is proposed. The approach, which is developed in the framework of a cooperation between the Plymouth Marine Laboratory (PML) and

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Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea

Cited by 34 publications

References 24 publications

The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

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