Paper Info
Title
Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network.
Abstract
Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.
Year
DOI
Venue
2019
10.3390/rs11020135
REMOTE SENSING
Keywords
Field
DocType
synthetic aperture radar (SAR),automatic target recognition (ATR),image segmentation,super-resolution generative adversarial network (SRGAN),deep convolutional neural network (DCNN)
Computer vision,Generative adversarial network,Automatic target recognition,Convolutional neural network,Synthetic aperture radar,Artificial intelligence,Geology,Superresolution
Journal
Volume
Issue
Citations 
11
2
0
PageRank 
References 
Authors
0.34
11
5
Name
Order
Citations
PageRank
Xiaoran Shi141.42
Feng Zhou22189158.01
yang shuang3104.30
Zi-jing Zhang413515.74
Tao Su500.68