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.

Statistical Analysis of the Detection Probability of the TianTuo-3 Space-based AIS

2017 ◽  
Vol 71 (2) ◽  
pp. 467-481 ◽  
Author(s):  
Shiyou Li ◽  
Lihu Chen ◽  
Xiaoqian Chen ◽  
Yong Zhao ◽  
Lei Yang
Keyword(s):  
Statistical Analysis ◽  
Detection Probability ◽  
High Probability ◽  
Distribution Density ◽  
Automatic Identification ◽  
Identification System ◽  
Receiving Antenna ◽  
Defense Technology ◽  
Vessel Detection ◽  
National University

The Micro-Nano satellite TianTuo-3 (TT-3) developed by the National University of Defense Technology (NUDT) was successfully launched on 20 September 2015. The space-based Automatic Identification System (AIS) on board TT-3 works well and stably receives AIS signals from global vessels. In this work, we perform statistical analysis on the detection probability of the vessels in the concerned areas by using the TT-3 AIS data. The results suggest that the detection probability of vessels decreases as the distribution density increases, especially in the offshore areas of dense traffic and the TT3-AIS vessel detection probability in the oceans can be higher than 40%, indicating that the TT-3 AIS has achieved a high probability of coverage of vessels for a single receiving antenna. The analysis results will present helpful references both in evaluating the potential application of satellite-based AIS and for designing the next generation space-based AIS which might greatly improve the detection probability of ocean-going vessels.

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.

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.

scholarly journals Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhelin Li ◽  
Lining Zhao ◽  
Xu Han ◽  
Mingyang Pan ◽  
Feng-Jang Hwang
Keyword(s):  
Spatial Separation ◽  
Experimental Results ◽  
Detection Methods ◽  
Automatic Identification ◽  
Identification System ◽  
Ship Detection ◽  
Detection Model ◽  
Backbone Network ◽  
Time Requirements ◽  
Detection Speed

Ship detection is one of the most important research contents of ship intelligent navigation and monitoring. As a supplement to classical navigational equipment such as radar and the Automatic Identification System (AIS), target detection based on computer vision and deep learning has become a new important method. A target detector called YOLOv3 has the advantages of detection speed and accuracy and meets the real-time requirements for ship detection. However, YOLOv3 has a large number of backbone network parameters and requires high hardware performance, which is not conducive to the popularization of applications. On the basis of YOLOv3, this paper proposes a lightweight ship detection model (LSDM) in which the backbone network is improved by using dense connection inspired from DenseNet, and the feature pyramid networks are improved by using spatial separation convolution to replace normal convolution. The two improvements reduce parameters and optimize the network structure greatly. The experimental results show that, with only one-third of parameters of YOLOv3, the LSDM has higher accuracy and speed for ship detection. In addition, the LSDM is simplified further by reducing the number of densely connected units to form a model called LSDM-tiny. The experimental results show that, LSDM-tiny has similar detection speed with YOLOv3-tiny, but has a lot higher accuracy.

scholarly journals Automated Procurement of Training Data for Machine Learning Algorithm on Ship Detection Using AIS Information

2020 ◽  
Vol 12 (9) ◽  
pp. 1443
Author(s):  
Juyoung Song ◽  
Duk-jin Kim ◽  
Ki-mook Kang
Keyword(s):  
Learning Algorithm ◽  
Doppler Frequency ◽  
Training Model ◽  
Training Data ◽  
Automatic Identification ◽  
Identification System ◽  
Visual Interpretation ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Model

Development of convolutional neural network (CNN) optimized for object detection, led to significant developments in ship detection. Although training data critically affect the performance of the CNN-based training model, previous studies focused mostly on enhancing the architecture of the training model. This study developed a sophisticated and automatic methodology to generate verified and robust training data by employing synthetic aperture radar (SAR) images and automatic identification system (AIS) data. The extraction of training data initiated from interpolating the discretely received AIS positions to the exact position of the ship at the time of image acquisition. The interpolation was conducted by applying a Kalman filter, followed by compensating the Doppler frequency shift. The bounding box for the ship was constructed tightly considering the installation of the AIS equipment and the exact size of the ship. From 18 Sentinel-1 SAR images using a completely automated procedure, 7489 training data were obtained, compared with a different set of training data from visual interpretation. The ship detection model trained using the automatic training data obtained 0.7713 of overall detection performance from 3 Sentinel-1 SAR images, which exceeded that of manual training data, evading the artificial structures of harbors and azimuth ambiguity ghost signals from detection.

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