Field truthing experiment of ship detection by using SAR imagery

2007 ◽  
Author(s):  
Junmin Meng ◽  
Jie Zhang ◽  
Changying Wang ◽  
Jungang Yang
Keyword(s):  
Ship Detection ◽  
Sar Imagery

A waterborne salient ship detection method on SAR imagery

2015 ◽  
Vol 58 (8) ◽  
pp. 1-3 ◽  
Author(s):  
Long Ma ◽  
Liang Chen ◽  
XueJing Zhang ◽  
He Chen ◽  
Nouman Qadeer Soomro
Keyword(s):  
Detection Method ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Ship detection in SAR imagery: A comparison study

2017 ◽  
Author(s):  
Pasquale Iervolino ◽  
Raffaella Guida ◽  
Parivash Lumsdon ◽  
Jurgen Janoth ◽  
Melanie Clift ◽  
...  
Keyword(s):  
Comparison Study ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Ship Detection Based on YOLOv2 for SAR Imagery

2019 ◽  
Vol 11 (7) ◽  
pp. 786 ◽  
Author(s):  
Yang-Lang Chang ◽  
Amare Anagaw ◽  
Lena Chang ◽  
Yi Wang ◽  
Chih-Yu Hsiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Experimental Results ◽  
Detection Methods ◽  
Computational Time ◽  
Detection Accuracy ◽  
Single Shot ◽  
Ship Detection ◽  
Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

scholarly journals Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 531 ◽  
Author(s):  
Yuanyuan Wang ◽  
Chao Wang ◽  
Hong Zhang ◽  
Yingbo Dong ◽  
Sisi Wei
Keyword(s):  
Class Imbalance ◽  
Weather Conditions ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ship Detection ◽  
Multi Scale ◽  
Resolution Imaging ◽  
Feature Pyramid ◽  
Sar Imagery ◽  
Ship Classification

Independent of daylight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect ships in marine surveillance. The shapes of ships are multi-scale in SAR imagery due to multi-resolution imaging modes and their various shapes. Conventional ship detection methods are highly dependent on the statistical models of sea clutter or the extracted features, and their robustness need to be strengthened. Being an automatic learning representation, the RetinaNet object detector, one kind of deep learning model, is proposed to crack this obstacle. Firstly, feature pyramid networks (FPN) are used to extract multi-scale features for both ship classification and location. Then, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. There are 86 scenes of Chinese Gaofen-3 Imagery at four resolutions, i.e., 3 m, 5 m, 8 m, and 10 m, used to evaluate our approach. Two Gaofen-3 images and one Constellation of Small Satellite for Mediterranean basin Observation (Cosmo-SkyMed) image are used to evaluate the robustness. The experimental results reveal that (1) RetinaNet not only can efficiently detect multi-scale ships but also has a high detection accuracy; (2) compared with other object detectors, RetinaNet achieves more than a 96% mean average precision (mAP). These results demonstrate the effectiveness of our proposed method.

scholarly journals Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery

2018 ◽  
Vol 15 (9) ◽  
pp. 1397-1401 ◽  
Author(s):  
Odysseas Pappas ◽  
Alin Achim ◽  
David Bull
Keyword(s):  
Ship Detection ◽  
Sar Imagery
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