A distributed automatic target recognition system using multiple low resolution sensors

2008 ◽  
Author(s):  
Zhanfeng Yue ◽  
Pramod Lakshmi Narasimha ◽  
Pankaj Topiwala
Keyword(s):  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Low Resolution

scholarly journals Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
David Vint ◽  
Matthew Anderson ◽  
Yuhao Yang ◽  
Christos Ilioudis ◽  
Gaetano Di Caterina ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Sar Images ◽  
Adversarial Networks ◽  
Technological Advances ◽  
Dataset Size ◽  
Resolution Imaging ◽  
High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

scholarly journals Hierarchical Recognition System for Target Recognition from Sparse Representations

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zongyong Cui ◽  
Zongjie Cao ◽  
Jianyu Yang ◽  
Hongliang Ren
Keyword(s):  
Deep Learning ◽  
Deep Structure ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Feature Representation ◽  
Small Data ◽  
Learning Method ◽  
Classification Problems ◽  
Restricted Boltzmann Machines

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.

Automatic target recognition system using high-resolution radar

1996 ◽  
Author(s):  
Steven P. Jacobs ◽  
Joseph A. O'Sullivan ◽  
Mohammad Faisal ◽  
Donald L. Snyder
Keyword(s):  
High Resolution ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
High Resolution Radar

Use of the mean-square-error matching metric in a model-based automatic target recognition system

1998 ◽  
Author(s):  
Stephen A. Stanhope ◽  
Eric R. Keydel ◽  
Wayne D. Williams ◽  
Vasik G. Rajlich ◽  
Russell Sieron
Keyword(s):  
Mean Square Error ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Mean Square ◽  
Model Based ◽  
The Mean
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