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

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...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Jg. 11; H. 5; S. 531
Hauptverfasser: Wang, Yuanyuan, Wang, Chao, Zhang, Hong, Dong, Yingbo, Wei, Sisi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2019
Schlagworte:
Accuracy
Clutter
Datasets
Deep learning
False alarms
Feature extraction
feature pyramid networks
focal loss
Gaofen-3 imagery
Image detection
International conferences
Machine learning
Mathematical models
Methods
Neural networks
Pattern recognition
Radar imaging
Remote sensing
Robustness
Satellite constellations
Satellite imagery
Satellite observation
Satellites
Sensors
Shape recognition
ship detection
Ships
Small satellites
Statistical analysis
Statistical models
Surveillance radar
Synthetic aperture radar
Weather
Beijing China
United States > US
China
Mediterranean Area
ISSN:2072-4292, 2072-4292
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs11050531