scholarly journals Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1078 ◽  
Author(s):  
Ramona Pelich ◽  
Marco Chini ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Carlos Lopez-Martinez ◽  
...  
Keyword(s):  
Detection Rate ◽  
Large Scale ◽  
Added Value ◽  
Automatic Identification ◽  
Identification System ◽  
Dual Polarization ◽  
Ship Detection ◽  
Traffic Conditions ◽  
Intensity Images ◽  
Polarization Channel

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

scholarly journals Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

2019 ◽  
Vol 11 (24) ◽  
pp. 2997 ◽  
Author(s):  
Clément Dechesne ◽  
Sébastien Lefèvre ◽  
Rodolphe Vadaine ◽  
Guillaume Hajduch ◽  
Ronan Fablet
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Large Scale ◽  
Visual Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Human Observer ◽  
Convolutional Network ◽  
Ship Detection

The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).

scholarly journals LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

2020 ◽  
Vol 12 (18) ◽  
pp. 2997 ◽  
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang ◽  
Xiao Ke ◽  
Xu Zhan ◽  
Jun Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Google Earth ◽  
Detection Methods ◽  
False Alarms ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Ship Detection ◽  
Small Ship

Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.

scholarly journals Analyzing Interactions between Japanese Ports and the Maritime Silk Road Based on Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Zhi-Hua Hu ◽  
Chan-Juan Liu ◽  
Paul Tae-Woo Lee
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Silk Road ◽  
Automatic Identification ◽  
Identification System ◽  
Belt And Road Initiative ◽  
Connectivity Analysis ◽  
Maritime Silk Road ◽  
Maritime Networks ◽  
Belt And Road

This article considers how the Japanese ports interact with the ports of China and along the 21st century Maritime Silk Road (MSR) while they are embedded in the global port network, especially in the context of China’s Belt and Road Initiative. At a port level, it primarily uses connectivity analysis to analyze the port relations and significances in the maritime network. In contrast, at the network level, it applies the methods from network sciences to analyze the significances of these maritime networks and the interactions among the maritime networks of Japan, China, and MSR. This article extracts a large-scale maritime network from ports and vessels’ profiles and data of vessels’ Automatic Identification System (AIS). It then examines the relations among the networks (including Japan, China, MSR, and global ports) after defining the maritime networks, network generation schemes, and port network analysis tools. Based on the analysis results and findings, this study draws some implications for regional ports and shipping development and the global supply network.

A Beam Scanning Method based on the Helical Antenna for Space-based AIS

2014 ◽  
Vol 68 (1) ◽  
pp. 52-70 ◽  
Author(s):  
Yun Cheng ◽  
Lihu Chen ◽  
Xiaoqian Chen
Keyword(s):  
Detection Probability ◽  
Automatic Identification ◽  
Identification System ◽  
Helical Antenna ◽  
Beam Scanning ◽  
Scanning Method ◽  
Ship Detection ◽  
Low Earth Orbits ◽  
Earth Orbits ◽  
Scanning Antenna

We investigate a strategy to address the problem of low ship detection probability of space-based Automatic Identification System (AIS). A directional AIS antenna and an innovative beam scanning method are proposed, which scan the antenna across a wide swath to provide complete coverage and maintain the advantage of a narrow footprint to reduce signal collision. Aiming at the mission requirement of global ship detection by the year 2016, the appropriate swath, the scanning range and the scanning rate were studied and designed in detail. Theoretical analysis and simulations showed that this scanning antenna can greatly improve ship detection probability and hold the detection probability at an average reporting interval from six to 15 seconds for most oceans when compared with the traditional fixed wide beam antenna. Furthermore, the detection capacity of this scanning antenna was little affected by the heights of different Low Earth Orbits. The results of this work show that the design of the helical antenna along with the beam scanning method can be considered as a building block of future space-based AIS.

scholarly journals DEEP LEARNING FOR VESSEL DETECTION AND IDENTIFICATION FROM SPACEBORNE OPTICAL IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 303-310
Author(s):  
G. Matasci ◽  
J. Plante ◽  
K. Kasa ◽  
P. Mousavi ◽  
A. Stewart ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Input Image ◽  
Automatic Identification ◽  
Identification System ◽  
Optical Remote Sensing ◽  
Detection Model ◽  
Model Based ◽  
Optical Images ◽  
Vessel Detection

Abstract. We present a deep learning-based vessel detection and (re-)identification approach from spaceborne optical images. We introduce these two components as part of a maritime surveillance from space pipeline and present experimental results on challenging real-world maritime datasets derived from WorldView imagery. First, we developed a vessel detection model based on RetinaNet achieving a performance of 0.795 F1-score on a challenging multi-scale dataset. We then collected a large-scale dataset for vessel identification by applying the detection model on 200+ optical images, detecting the vessels therein and assigning them an identity via an Automatic Identification System association framework. A vessel re-identification model based on Twin neural networks has then been trained on this dataset featuring 2500+ unique vessels with multiple repeated occurrences across different acquisitions. The model allows to naturally establish similarities between vessel images. It returns a relevant ranking of candidate vessels from a database when provided an input image for a specific vessel the user might be interested in, with top-1 and top-10 accuracies of 38.7% and 76.5%, respectively. This study demonstrates the potential offered by the latest advances in deep learning and computer vision when applied to optical remote sensing imagery in a maritime context, opening new opportunities for automated vessel monitoring and tracking capabilities from space.

scholarly journals Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Maximilian Rodger ◽  
Raffaella Guida
Keyword(s):  
Data Fusion ◽  
Large Scale ◽  
Data Association ◽  
Classification Model ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Surveillance ◽  
Research Activities ◽  
Wide Range ◽  
Sar Imagery

A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.

scholarly journals AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres

2021 ◽  
pp. 1-24
Author(s):  
Arnstein Vestre ◽  
Azzeddine Bakdi ◽  
Erik Vanem ◽  
Øystein Engelhardtsen
Keyword(s):  
Collision Avoidance ◽  
Traffic Control ◽  
Large Scale ◽  
Technological Development ◽  
Algorithm Design ◽  
Automatic Identification ◽  
Identification System ◽  
Real World Data ◽  
Data Set ◽  
Maritime Traffic

Abstract Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

scholarly journals A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2756
Author(s):  
Alessandro Galdelli ◽  
Adriano Mancini ◽  
Carmen Ferrà ◽  
Anna Nora Tassetti
Keyword(s):  
Large Scale ◽  
Automatic Identification ◽  
Identification System ◽  
Distributed Data ◽  
Close Proximity ◽  
Geographically Distributed ◽  
Remote Sensing Systems ◽  
Spatio Temporal ◽  
Sar Imagery ◽  
Point To Point

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.

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

2013 ◽  
Vol 67 (1) ◽  
pp. 177-189 ◽  
Author(s):  
Zhi Zhao ◽  
Kefeng Ji ◽  
Xiangwei Xing ◽  
Huanxin Zou ◽  
Shilin Zhou
Keyword(s):  
State Of The Art ◽  
Rapid Development ◽  
Automatic Identification ◽  
Identification System ◽  
Environment Monitoring ◽  
Ship Detection ◽  
Maritime Surveillance ◽  
Satisfactory Performance ◽  
Detection And Identification ◽  
Ocean Environment

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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