Discriminative Feature Learning for Real-Time SAR Automatic Target Recognition With the Nonlinear Analysis Cosparse Model

2018 ◽  
Vol 15 (7) ◽  
pp. 1045-1049 ◽  
Author(s):  
Zaidao Wen ◽  
Biao Hou ◽  
Qian Wu ◽  
Licheng Jiao
Keyword(s):  
Nonlinear Analysis ◽  
Real Time ◽  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Discriminative Feature

scholarly journals Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

2021 ◽  
Vol 13 (8) ◽  
pp. 1455
Author(s):  
Jifang Pei ◽  
Weibo Huo ◽  
Chenwei Wang ◽  
Yulin Huang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Single View ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

scholarly journals Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

2021 ◽  
Vol 13 (18) ◽  
pp. 3554
Author(s):  
Xiaowei Hu ◽  
Weike Feng ◽  
Yiduo Guo ◽  
Qiang Wang
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Feature Space ◽  
Automatic Target Recognition ◽  
Data Sets ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Public Data ◽  
Class Labels

Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.

scholarly journals Real-Time Underwater Maritime Object Detection in Side-Scan Sonar Images Based on Transformer-YOLOv5

2021 ◽  
Vol 13 (18) ◽  
pp. 3555
Author(s):  
Yongcan Yu ◽  
Jianhu Zhao ◽  
Quanhua Gong ◽  
Chao Huang ◽  
Gen Zheng ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Target Localization ◽  
Average Precision ◽  
Image Preprocessing ◽  
Side Scan Sonar ◽  
Sonar Images ◽  
Underwater Target

To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image.

Complete automatic target recognition system for real-time human face images

1998 ◽  
Author(s):  
Haisong Liu ◽  
Minxian Wu ◽  
Guofan Jin ◽  
Gang Cheng ◽  
Qingsheng He
Keyword(s):  
Real Time ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Human Face ◽  
Face Images
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