Rotated ship target detection algorithm in SAR images based on global feature fusion

被引：0
作者：
Xue, Fengtao [1 ]
Sun, Tianyu [2 ]
Yang, Yimin [2 ]
Yang, Jian [2 ]
机构：
[1] Beijing Institute of Remote Sensing Equipment, Beijing
[2] Department of Electronic Engineering, Tsinghua University, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 12期
关键词：
feature fusion; neural network; rotated target detection; ship detection; synthetic aperture radar (SAR);
D O I：
10.12305/j.issn.1001-506X.2024.12.13
中图分类号：
学科分类号：
摘要：
Conventional models are not effective in detecting inshore rotated ship targets in synthetic aperture radar (SAR) images. To solve this problem, a method of detecting rotated ship targets in SAR images based on global feature fusion is proposed. Firstly, the global attention feature pyramid network is used to fuse features of different levels, which shortens the transmission path from the bottom feature to the top feature. Secondly, positional embedding is added in the image block fusion stage to reduce the loss of location information caused by down-sampling. Finally, the rotated feature alignment network is used to generate high-quality anchor points and rotated alignment features for Classification and coordinate regression. The proposed method achieves an average detection precision of 0. 894 8 on the rotated ship detection dataset in SAR images (RSDD-SAR) dataset when the rotated intersection over union (IoU) is 0. 5. The proposed method has good detection Performance for both inshore and offshore ships. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：4044 / 4053
页数：9
相关论文
共 30 条
  • [1] DENG Y K, ZHAC) F J, WANG Y., Brief analysis on the develop-ment and application of spaceborne SAR, Journal of Radars, 1, 1, pp. 1-10, (2012)
  • [2] DENG Y K, YUWD, ZHANG H, Et al., Forthcoming spaceborne SAR development, Journal of Radars, 9, 1, pp. 1-33, (2020)
  • [3] GIOVANNI M P, MARCO M, STEFANO T, Et al., Very high resolution automotive SAR imaging front burst data, Remote Sensing, 15, 3, pp. 845-864, (2023)
  • [4] LI Z Y., Study on high squint imaging algorithms for SAR mounted on maneuvering platforms, (2017)
  • [5] YAN G X, CHEN Z H, WANG Y, Et al., LssDet: a lightweight deep learning detector for SAR ship detection in high-resolution SAR images, Remote Sensing, 14, 20, pp. 5148-5166, (2022)
  • [6] ZHAO K, LUR T, WANG S Y, Et al., ST-YOLOA. a Swin-Transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background, Frontiers in Neurorobotics, 17, pp. 1170163-1170177, (2023)
  • [7] LI T., Study on ship detection methods for SAR images, (2019)
  • [8] LIU T, YANG Z Y, YANG J, Et al., CFAR ship detection methods using compact Polarimetrie SAR in a K-Wishart distribution, IEEE Journal of Selected Topics in Applied Earth Observation* and Remote Sensing, 12, 10, pp. 3737-3745, (2019)
  • [9] LI J W, CHEN J, CHENG P, Et al., A survey on deep-learning-ba.sed real-time SAR ship detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp. 3218-3247, (2023)
  • [10] UN T Y, GOYAL P, GIRSHICK R, Et al., Focal loss for dense objeet detection, Proc. of the IEEE International Conference on Computer Vision, pp. 2980-2988, (2017)
123