Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images

Graziano, Maria Daniela; Renga, Alfredo

doi:10.3390/rs13101955

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…The research field of automated vessel detection has been rapidly advancing, with the improvement of deep learning techniques for object detection (Cheng et al, 2016;Kang et al, 2017;Liu et al, 2021a) and the adoption of novel approaches, such as use of wakes behind moving vessels (Graziano and Renga, 2021;Liu et al, 2021b) and multispectral imagery (Wang et al, 2021). The field of vessel detection will further advance with the increasing accessibility to super high-resolution imagery (Jeŕome, 2019), which could allow the classification of vessel types and fishing gears in the near future.…”

Section: Discussionmentioning

confidence: 99%

Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery

et al. 2023

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Monitoring marine use is essential to effective management but is extremely challenging, particularly where capacity and resources are limited. To overcome these limitations, satellite imagery has emerged as a promising tool for monitoring marine vessel activities that are difficult to observe through publicly available vessel-tracking data. However, the broader use of satellite imagery is hindered by the lack of a clear understanding of where and when it would bring novel information to existing vessel-tracking data. Here, we outline an analytical framework to (1) automatically detect marine vessels in optical satellite imagery using deep learning and (2) statistically contrast geospatial distributions of vessels with the vessel-tracking data. As a proof of concept, we applied our framework to the coastal regions of Peru, where vessels without the Automatic Information System (AIS) are prevalent. Quantifying differences in spatial information between disparate datasets—satellite imagery and vessel-tracking data—offers insight into the biases of each dataset and the potential for additional knowledge through data integration. Our study lays the foundation for understanding how satellite imagery can complement existing vessel-tracking data to improve marine oversight and due diligence.

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Section: Discussionmentioning

confidence: 99%

Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery

et al. 2023

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“…The paper also presents the problem that sea artifacts may bring in causing False Positives. While the assumption of considering the sea surface uniform is valid, this is valid only in limited circumstances such as in open sea scenarios and in situations with low wind conditions as discussed also by Reference [25]. Sea artifacts were found in a dataset composed of only thirteen sea images and it is therefore believed that they should not be considered as a rare event but instead be included in any algorithm dealing with wake detection through line identifications.…”

Section: Discussionmentioning

confidence: 99%

“…The field of SAR imagery usage for vessel detection through wake identification is instead a less studied one. References [4,25,26] are the few studies presenting an algorithm for wake detection through SAR imagery. The algorithm presented by M.D.…”

Section: Introductionmentioning

confidence: 99%

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A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery

Grosso

Guida

2022

Remote Sensing

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In the field of maritime surveillance with satellite imagery, ship detection with Synthetic Aperture Radar (SAR) is largely successful with the exception of those cases where the target Radar Cross Section (RCS) results very low and hard to detect. The paper presents an automatic algorithm that tackles the problem of ship detection in situations where the ship’s RCS is not measurable but its wake is. At this purpose, the algorithm proposed uses a combination of image processing techniques in order to identify ships exclusively by detecting the wakes they leave behind. Once a ship is identified through its wake, its position in the image, its heading and its speed are determined. The algorithm was developed, tested and validated on SAR imagery from the European Space Agency mission Sentinel-1; the results are here presented and discussed. The algorithm was validated on a total of 43 images and resulted in a ship detection rate of 93% with wakes’ components recognized in more than 80% of the cases analysed.

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“…The imaging characteristics and detection of a far-field turbulent wake have been studied extensively [6][7][8], but little research has been conducted on the near-field turbulent wake. In a previous study, a numerical model of turbulence with small momentum was built, and the relationship between the excess momentum and flow characteristics of a turbulent wake was studied [9,10].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Scattering of Near-Field Turbulent Wake Generated by Accelerated Propeller

et al. 2021

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The electromagnetic scattering study of the turbulent wake of a moving ship has important application value in target recognition and tracking. However, to date, there has been insufficient research into the electromagnetic characteristics of near-field propeller turbulence. This study presents a new procedure for evaluating the electromagnetic scattering coefficient and imaging characteristics of turbulent wakes in the near field. By controlling the different values of the net momenta, a turbulent wake was generated using the large-eddy simulation method. The results show that the net momentum transferred to the background flow field determines the development of the turbulent wake, which explains the formation mechanism of the turbulence. Combined with the turbulent energy attenuation spectrum, the electromagnetic scattering characteristics of the turbulent wake were calculated using the two-scale facet mode. Using this method, the impact of different parameters on the scattering coefficient and the electromagnetic image of the turbulence wake were investigated, to explain the modulation mechanism and electromagnetic imaging characteristics of the near-field turbulent wake. Moreover, an application for estimating a ship’s heading is proposed based on the electromagnetic imaging characteristics of the turbulent wake.

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Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images

Cited by 13 publications

References 26 publications

Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery

Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery

A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery

Electromagnetic Scattering of Near-Field Turbulent Wake Generated by Accelerated Propeller

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