Scattering feature extraction and fuse network for aircraft detection in synthetic aperture radar images

被引:2
作者
Chen, Ting [1 ]
Huang, Xiaohong [1 ]
Lin, Sizhe [1 ]
机构
[1] Sun Yat Sen Univ, Sch Elect & Commun Engn, Shenzhen, Peoples R China
关键词
synthetic aperture radar; aircraft detection; scattering feature; efficient channel attention; feature fusion;
D O I
10.1117/1.JRS.17.026517
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Synthetic aperture radar (SAR) aircraft detection methods based on deep learning have become a current research hotspot. However, considerable challenges still remain due to the scattering feature of aircraft, variations in aircraft size, and interference from complex scenarios. To tackle these problems, the scattering feature extraction and fuse network (SFEF-Net) is proposed. First, considering the scattering characteristics of aircraft, we propose a scattering feature extraction and relation enhancement (SFERE) backbone based on the deformable convolution and the global context block. The SFERE backbone is used to extract the scattering feature of aircraft and model the correlation of scattering points. Furthermore, to enhance the detection ability for multi-scale aircraft targets in complex scenes, we redesign an attention bidirectional feature fusion pyramid (ABFFP). Two novel modules are proposed in ABFFP, namely, the attention guidance feature fusion (AGFF) module and the residual efficient channel attention (RECA) module. The AGFF module is proposed to suppress the interference of backgrounds and aggregate the multi-level feature maps. After the feature fusion operation, the output feature maps contain richer channel information, but there is some redundant information that could reduce the accuracy. Therefore, we adopt the RECA module to further select useful information in the channel dimension. To demonstrate the effectiveness of SFEF-Net, SAR aircraft images from the Gaofen-3 system are utilized in the experiments. The detection results show that the proposed model achieves competitive performance with an average precision of 95.5%.
引用
收藏
页数:16
相关论文
共 40 条
[1]   DRBox-v2: An Improved Detector With Rotatable Boxes for Target Detection in SAR Images [J].
An, Quanzhi ;
Pan, Zongxu ;
Liu, Lei ;
You, Hongjian .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (11) :8333-8349
[2]   Cascade R-CNN: Delving into High Quality Object Detection [J].
Cai, Zhaowei ;
Vasconcelos, Nuno .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :6154-6162
[3]   GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond [J].
Cao, Yue ;
Xu, Jiarui ;
Lin, Stephen ;
Wei, Fangyun ;
Hu, Han .
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), 2019, :1971-1980
[4]  
Chen K, 2019, Arxiv, DOI arXiv:1906.07155
[5]   Geospatial Transformer Is What You Need for Aircraft Detection in SAR Imagery [J].
Chen, Lifu ;
Luo, Ru ;
Xing, Jin ;
Li, Zhenhong ;
Yuan, Zhihui ;
Cai, Xingmin .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[6]   Deformable Convolutional Networks [J].
Dai, Jifeng ;
Qi, Haozhi ;
Xiong, Yuwen ;
Li, Yi ;
Zhang, Guodong ;
Hu, Han ;
Wei, Yichen .
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, :764-773
[7]   Res2Net: A New Multi-Scale Backbone Architecture [J].
Gao, Shang-Hua ;
Cheng, Ming-Ming ;
Zhao, Kai ;
Zhang, Xin-Yu ;
Yang, Ming-Hsuan ;
Torr, Philip .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (02) :652-662
[8]  
Ge Z, 2021, Arxiv, DOI [arXiv:2107.08430, 10.48550/arXiv.2107.08430, DOI 10.48550/ARXIV.2107.08430]
[9]   Fast R-CNN [J].
Girshick, Ross .
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :1440-1448
[10]   Rich feature hierarchies for accurate object detection and semantic segmentation [J].
Girshick, Ross ;
Donahue, Jeff ;
Darrell, Trevor ;
Malik, Jitendra .
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, :580-587