4.7 Article

Ship Detection in Large-Scale SAR Images Via Spatial Shuffle-Group Enhance Attention

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2997200

关键词

Marine vehicles; Radar polarimetry; Object detection; Feature extraction; Synthetic aperture radar; Semantics; Neural networks; CenterNet; large-scale scene; ship detection; spatial shuffle-group enhance; synthetic aperture radar (SAR)

资金

  1. National Natural Science Foundation of China [61801098, 61971101]
  2. Shanghai Aerospace Science and Technology Innovation Fund [SAST2018-079]
  3. Science and Technology on Automatic Target Recognition Laboratory (ATR) Fund [6142503190201]

向作者/读者索取更多资源

A ship detection method in large-scale SAR images via CenterNet is proposed, which defines the target as a point and locates the center point of the target through key point estimation to avoid missing small targets, and reduces false alarms through the introduction of SSE attention module. Experimental results show that the proposed method can detect all targets in dense-docking conditions.
Ship target detection using large-scale synthetic aperture radar (SAR) images has important application in military and civilian fields. However, ship targets are difficult to distinguish from the surrounding background and many false alarms can occur due to the influence of land area. False alarms always occur with ship target detection because most of the area in large-scale SAR images are treated as background and clutter, and the ship targets are considered unevenly distributing small targets. To address these issues, a ship detection method in large-scale SAR images via CenterNet is proposed in this article. As an anchor-free method, CenterNet defines the target as a point, and the center point of the target is located through key point estimation, which can effectively avoid the missing detection of small targets. At the same time, the spatial shuffle-group enhance (SSE) attention module is introduced into CenterNet. Through SSE, the stronger semantic features are extracted while suppressing some noise to reduce false positives caused by inshore and inland interferences. The experiments on the public SAR-ship-data set show that the proposed method can detect all targets without missed detection with dense-docking targets. For the ship targets in large-scale SAR images from Sentinel 1, the proposed method can also detect targets near the shore and in the sea with high accuracy, which outperforms the methods like faster R-convolutional neural network (CNN), single-shot multibox detector (SSD), you only look once (YOLO), feature pyramid network (FPN), and their variations.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据