A Precise Oriented Ship Detector in SAR Images Based on Dynamic Rotated Positive Sample Mining

Abstract

The synthetic aperture radar (SAR) oriented ship detection is a more complex and finer task than the horizontal, and it provides ship orientation for military and civilian applications, such as trajectory prediction. However, many oriented target detection methods discarded high-resolution (low-level) feature map, which provides morphological features for predicting accurate bounding box for small ships, due to computational cost and lack of semantic information. In addition, most methods follow the samples assignment in horizontal detection after padding oriented bounding box. The inaccurate geometric relationship causes that some low-quality positive samples without contributing accuracy improvement are introduced. To improve the precision of small oriented ship detection, we proposed the feature pyramid network contained high-resolution feature to fuse high-level semantics and highlight contours and textures of ships in the low-level feature map. To select high-quality positive rotated samples, dynamic rotated positive samples mining (DRPSM) is designed to mine hard positive samples for oriented ship detection specially. In one word, a two-stage and anchor-free detector trained by DRPSM and utilized high-resolution feature map is proposed for detecting oriented ships in SAR imagery. Subsequently, extensive experiments have been conducted on two oriented SAR datasets: the SSDD and the HRSID. As a result, our method attains recall of 96.2% and 90.1%, and AP50 of 90.7% and 86.5% on SSDD and HRSID, respectively. It shows that our detector achieves state-of-the-art results applied in capturing ships, when compared with other popular remote sensing oriented detectors.

Keywords

• Convolution neural network (CNN)
• oriented detection
• remote sensing
• ship detection
• synthetic aperture radar (SAR)