Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism

被引：3

作者：

Tang, Tao ^{[1
]}

Cui, Yuting ^{[2
]}

Feng, Rui ^{[3
]}

Xiang, Deliang ^{[3
]}

机构：

[1] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China

[2] Ceyear Technol Co Ltd, Qingdao 266555, Peoples R China

[3] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100013, Peoples R China

来源：

INFORMATION | 2024年 / 15卷 / 03期

关键词：

mixed attention mechanism; MA-MobileNetV2; vehicle target recognition; synthetic aperture radar (SAR); CLASSIFICATION; CNN;

D O I：

10.3390/info15030159

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in existing SAR image target recognition focuses on spatial and channel information but lacks research on the relationship and recognition mechanism between spatial and channel information. In response to this issue, this article proposes a hybrid attention module and introduces a Mixed Attention (MA) mechanism module in the MobileNetV2 network. The proposed MA mechanism fully considers the comprehensive calculation of spatial attention (SPA), channel attention (CHA), and coordinated attention (CA). It can input feature maps for comprehensive weighting to enhance the features of the regions of interest, in order to improve the recognition rate of vehicle targets in SAR images.The superiority of our algorithm was verified through experiments on the MSTAR dataset.

引用

页数：18

共 31 条

[11] RETRACTED: Automatic Target Recognition of SAR Images Using Collaborative Representation (Retracted Article) [J].

Hu, Jinge .

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022

[12] SAR Target Configuration Recognition Using Tensor Global and Local Discriminant Embedding [J].

Huang, Xiayuan ;

Qiao, Hong ;

Zhang, Bo .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (02) :222-226

[13] Speckle-Noise-Invariant Convolutional Neural Network for SAR Target Recognition [J].

Kwak, Youngchul ;

Song, Woo-Jin ;

Kim, Seong-Eun .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2019, 16 (04) :549-553

[14] LW-CMDANet: A Novel Attention Network for SAR Automatic Target Recognition [J].

Lang, Ping ;

Fu, Xiongjun ;

Feng, Cheng ;

Dong, Jian ;

Qin, Rui ;

Martorella, Marco .

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 :6615-6630

[15] SAR Target Recognition Based on Efficient Fully Convolutional Attention Block CNN [J].

Li, Rui ;

Wang, Xiaodan ;

Wang, Jian ;

Song, Yafei ;

Lei, Lei .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19

[16] A Gradually Distilled CNN for SAR Target, Recognition [J].

Min, Rui ;

Lan, Hai ;

Cao, Zongjie ;

Cui, Zongyong .

IEEE ACCESS, 2019, 7 :42190-42200

[17] Multiview Synthetic Aperture Radar Automatic Target Recognition Optimization: Modeling and Implementation [J].

Pei, Jifang ;

Huang, Yulin ;

Sun, Zhichao ;

Zhang, Yin ;

Yang, Jianyu ;

Yeo, Tat-Soon .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56 (11) :6425-6439

[18] MobileNetV2: Inverted Residuals and Linear Bottlenecks [J].

Sandler, Mark ;

Howard, Andrew ;

Zhu, Menglong ;

Zhmoginov, Andrey ;

Chen, Liang-Chieh .

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :4510-4520

[19] A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification [J].

Shao, Jiaqi ;

Qu, Changwen ;

Li, Jianwei ;

Peng, Shujuan .

SENSORS, 2018, 18 (09)

[20] Classification via weighted kernel CNN: application to SAR target recognition [J].

Tian, Zhuangzhuang ;

Wang, Liping ;

Zhan, Ronghui ;

Hu, Jiemin ;

Zhang, Jun .

INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (23) :9249-9268

← 1 2 3 4 →