A review of recent advance of ship detection in single-channel SAR images

Abstract

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD).

Keywords:

• Synthetic aperture radar (SAR)
• ship detection
• single-channel
• deep learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the NSFC Project under Grant 61771142.