Detecting rotated ships in SAR images using a streamlined ship detection network and gliding phases

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-03-25 DOI:10.1080/2150704x.2024.2334193

Wenbo Yu, Jiamu Li, Zijian Wang, Zhongjun Yu, Yunhua Luo, Yabo Liu, Jie Feng

引用次数: 0

Abstract

Deep learning methods have greatly promoted the ship detection performance in synthetic aperture radar (SAR) images. However, due to the different imaging mechanisms and target characteristics, det...

查看原文本刊更多论文

利用精简的船舶探测网络和滑行阶段探测合成孔径雷达图像中的旋转船舶

深度学习方法大大提高了合成孔径雷达（SAR）图像中的船舶探测性能。然而，由于成像机制和目标特征的不同，探测船舶的方法也不尽相同。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.