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

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 5; p. 531
• Main Authors: Wang, Yuanyuan, Wang, Chao, Zhang, Hong, Dong, Yingbo, Wei, Sisi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2019
• Subjects: 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

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...