SAR image target recognition based on non-local operation

Zhihui Xin; Mengting Yuan; Yongxin Li; Yu Sun; Jiayu Xuan; Wei Ma; Yangxiu Liu; Zhixu Wang

doi:10.1117/12.2620620

8 July 2022 SAR image target recognition based on non-local operation

Zhihui Xin, Mengting Yuan, Yongxin Li, Yu Sun, Jiayu Xuan, Wei Ma, Yangxiu Liu, Zhixu Wang

Author Affiliations +

Proceedings Volume 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 122810H (2022) https://doi.org/10.1117/12.2620620
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only

Abstract

Synthetic aperture radar (SAR) target recognition is an important part of SAR image interpretation. It has been widely used in the field of national defense and national economy. At present, the feature extraction based on convolution is a local operation in space and time. The convolution kernel extracts the features in the local region with a certain step size. The global information of the picture can only be obtained by increasing the number of convolution layers. However, this method will not only increase the difficulty of model training but also makes the optimization of the network more difficult, and even leads to over fitting. Therefore, this paper proposes a SAR image target recognition algorithm based on GoogLeNet -NB, which combines accurate and effective GoogLeNet framework and non-local block(NB). By adding NB to GoogLeNet framework, we can capture more context information, enhance the correlation between pixels and regions, and improve the representation ability of the network. In order to verify the effectiveness of NB, NB is added in different positions of GoogLeNet framework for experimental comparison. Finally, MSTAR database is used to verify the algorithm. The experimental results show that the recognition effect of the GoogLeNet -NB algorithm model proposed in this paper is better than the traditional Alexnet algorithm and GoogLeNet algorithm. In the adding NB in different positions of GoogLeNet framework, adding NB in the front position of GoogLeNet can reduce the loss of information in the training process and obtain more global information. Therefore, GoogLeNet-preNB algorithm has certain advantages over GoogLeNet-postNB algorithm.

Citation Download Citation

Zhihui Xin, Mengting Yuan, Yongxin Li, Yu Sun, Jiayu Xuan, Wei Ma, Yangxiu Liu, and Zhixu Wang "SAR image target recognition based on non-local operation", Proc. SPIE 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 122810H (8 July 2022); https://doi.org/10.1117/12.2620620

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Synthetic aperture radar

Target recognition

Convolution

Data modeling

Image processing

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years