An information system for ship detection and identification

Ross, Edward Halford; Arifin, Bustanul; Brodsky, Yuval

doi:10.1109/igarss.2011.6049574

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Additionally, Margarit et al (2009a, 2009b, 2009c) presented the operative Ship Monitoring with SAR (SIMONS) system integrating space-borne SAR data and transponder polls (mainly AIS) to show accurate ship monitoring capabilities with added-value information layers. Ross et al (2011) designed SDIS (Ship Detection and Identification System), integrating many existing data sources which are readily available either commercially or through partnerships with governmental organisations including space-borne SAR, AIS, VMS/LRIT, acoustic underwater and electric field detector, ship-based and coastal radar, to mitigate the maritime issues. Arifin et al (2011) researched data security in SDIS, using a data-at-rest approach and encrypting the data streams within SDIS at all stages including transmission, storage and in-use.…”

Section: Reviewmentioning

confidence: 99%

Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research

Zhao

Xing

et al. 2013

J. Navigation

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Ship surveillance is important for maritime security and safety. It plays important roles in many applications including ocean environment monitoring, search and rescue, anti-piracy and military reconnaissance. Among various sensors used for maritime surveillance, space-borne Synthetic Aperture Radar (SAR) is valued for its high resolution over wide swaths and all-weather working capabilities. However, the state-of-the-art algorithms for ship detection and identification do not always achieve a satisfactory performance. With the rapid development of space-borne Automatic Identification System (AIS), near real-time and global surveillance has become feasible. However, not all ships are equipped with or operate AIS. Space-borne SAR and AIS are considered to be complementary, and ship surveillance using an integrated combination has attracted much attention. In order to summarize the achievements and present references for further research, this paper attempts to explicitly review the developments in previous research as the basis of a brief introduction to space-borne SAR and AIS.

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Section: Reviewmentioning

confidence: 99%

Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research

Zhao

Xing

et al. 2013

J. Navigation

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“…Sea pollution can be detected with the help of environmental monitoring. Also, restricted fishing areas can be better controlled by fishery management systems using remote sensing in marine engineering [4].…”

Section: Introductionmentioning

confidence: 99%

A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Uçar

Korkmaz

2020

Sakarya University Journal of Science

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Ship detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, the intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship detection. In this study, we propose a Deep Convolutional Neural Network (DCNN) model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image with a deep learning model built from scratch. The designed CNN model is constructed with a plain and easy to implement form in particular to the preferred satellite image set. Visual and graphical results show that the proposed model provides an efficient detection process with an accuracy of 99.60%.

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“…The related research includes the design and control of UAV research [16], business application mode and method research [17], task-oriented path planning and collaborative operation research [18,19]. Ross et al [20] introduced a ship detection and identification system based on the fusion of multiple sensors (including vessel AIS equipment, satellite imagery and radar images). Habtemariam et al [21] proposed a radar and AIS information fusion algorithm based on the joint probability data protocol framework.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Xiu

Wen²,

Yuan

et al. 2019

Sensors

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In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel’s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

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An information system for ship detection and identification

Cited by 5 publications

References 3 publications

Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research

Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research

A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

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