scholarly journals MARITIME SURVEILLANCE USING SAR OCEANIC IMAGES

2021 ◽  
Vol 9 (2) ◽  
pp. 1200-1205
Author(s):  
DR.J.Chenni Kumaran, Et. al.
Keyword(s):  
Ship Detection ◽  
Maritime Surveillance ◽  
Marine Protection ◽  
Wake Detection ◽  
User Friendly ◽  
Protection Authority

Marine things to do play a necessary position in the improvement of a nation. There is, however, a possible threat to the protection of a country via marine routes. This paper provides the most effective and easy-to-use method that enables the defense and marine protection authority staff to track the middle sections of the ocean for unusual marine activities by collecting an oceanic image from a SAR satellite. It helps the user by extracting information such as number of ships at particular region, distance between each, length and breadth of ships and the velocity at which the ship is moving. It includes various modules such as initial tiling and pre-processing, vessel or ship detection and wake detection for estimating velocity of ship. The interface of this framework is very user-friendly and fully implemented with a MATLAB.

Ship detection using SAR and AIS raw data for maritime surveillance

2016 ◽  
Author(s):  
Fabio Manzoni Vieira ◽  
Francois Vincent ◽  
Jean-Yves Tourneret ◽  
David Bonacci ◽  
Marc Spigai ◽  
...  
Keyword(s):  
Raw Data ◽  
Ship Detection ◽  
Maritime Surveillance

scholarly journals Integration of Fine Model-Based Decomposition and Guard Filter for Ship Detection in PolSAR Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4295
Author(s):  
Dongsheng Liu ◽  
Ling Han
Keyword(s):  
Figure Of Merit ◽  
Spatial Information ◽  
Synthetic Aperture ◽  
Widespread Application ◽  
Polarimetric Synthetic Aperture Radar ◽  
Ship Detection ◽  
Qualitative And Quantitative ◽  
Decomposition Scheme ◽  
Model Based ◽  
Maritime Surveillance

Ship detection with polarimetric synthetic aperture radar (PolSAR) has gained extensive attention due to its widespread application in maritime surveillance. Nevertheless, designing identifiable features to realize accurate ship detection is still challenging. For this purpose, a fine eight-component model-based decomposition scheme is first presented by incorporating four advanced physical scattering models, thus accurately describing the dominant and local structure scattering of ships. Through analyzing the exclusive scattering mechanisms of ships, a discriminative ship detection feature is then constructed from the derived contributions of eight kinds of scattering components. Combined with a spatial information-based guard filter, the efficacy of the feature is further amplified and thus a ship detector is proposed which fulfills the final ship detection. Several qualitative and quantitative experiments are conducted on real PolSAR data and the results demonstrate that the proposed method reaches the highest figure-of-merit (FoM) factor of 0.96, which outperforms the comparative methods in ship detection.

scholarly journals Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chun Liu ◽  
Jian Li
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Target Object ◽  
Variable Time ◽  
Ship Detection ◽  
Detection Model ◽  
Maritime Surveillance ◽  
Model Training ◽  
Direction Judgment ◽  
Deep Learning Model

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.

Machine learning-based ship detection and tracking using satellite images for maritime surveillance

2021 ◽  
Vol 13 (5) ◽  
pp. 361-371
Author(s):  
Yu Wang ◽  
G. Rajesh ◽  
X. Mercilin Raajini ◽  
N. Kritika ◽  
A. Kavinkumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Satellite Images ◽  
Google Earth ◽  
Support Vector ◽  
Linear Discriminant ◽  
Ship Detection ◽  
Maritime Surveillance ◽  
Detection And Tracking ◽  
Multiple Hypothesis Tracking

The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

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.

An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system

2021 ◽  
Vol 235 ◽  
pp. 109435
Author(s):  
Ryan Wen Liu ◽  
Weiqiao Yuan ◽  
Xinqiang Chen ◽  
Yuxu Lu
Keyword(s):  
Surveillance System ◽  
Learning Method ◽  
Ship Detection ◽  
Maritime Surveillance

scholarly journals Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 660
Author(s):  
Liqiong Chen ◽  
Wenxuan Shi ◽  
Dexiang Deng
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Maritime Surveillance ◽  
Detection Speed

Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.

Ship detection for visual maritime surveillance from non-stationary platforms

2017 ◽  
Vol 141 ◽  
pp. 53-63 ◽  
Author(s):  
Yang Zhang ◽  
Qing-Zhong Li ◽  
Feng-Ni Zang
Keyword(s):  
Ship Detection ◽  
Maritime Surveillance
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