scholarly journals Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network

2021 ◽  
Vol 13 (21) ◽  
pp. 4358
Author(s):  
Chuan Du ◽  
Lei Zhang
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Reconstruction Error ◽  
Sar Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Scattering Centers ◽  
Weak Scattering ◽  
Adversarial Examples ◽  
Adversarial Attack

Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system output the intended wrong label predictions by adding small adversarial perturbations to the SAR images. The existing optimization-based adversarial attack methods generate adversarial examples by minimizing the mean-squared reconstruction error, causing smooth target edge and blurry weak scattering centers in SAR images. In this paper, we build a UNet-generative adversarial network (GAN) to refine the generation of the SAR-ATR models’ adversarial examples. The UNet learns the separable features of the targets and generates the adversarial examples of SAR images. The GAN makes the generated adversarial examples approximate to real SAR images (with sharp target edge and explicit weak scattering centers) and improves the generation efficiency. We carry out abundant experiments using the proposed adversarial attack algorithm to fool the SAR-ATR models based on several advanced CNNs, which are trained on the measured SAR images of the ground vehicle targets. The quantitative and qualitative results demonstrate the high-quality adversarial example generation and excellent attack effectiveness and efficiency improvement.

scholarly journals Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

2019 ◽  
Vol 11 (2) ◽  
pp. 135 ◽  
Author(s):  
Xiaoran Shi ◽  
Feng Zhou ◽  
Shuang Yang ◽  
Zijing Zhang ◽  
Tao Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Super Resolution ◽  
Automatic Target Recognition ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Feature Characterization

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.

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 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 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.

Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

2021 ◽  
Vol 263 (2) ◽  
pp. 4558-4564
Author(s):  
Minghong Zhang ◽  
Xinwei Luo
Keyword(s):  
Data Augmentation ◽  
Target Recognition ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Acoustic Target ◽  
The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

scholarly journals Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hongqiao Wang ◽  
Yanning Cai ◽  
Guangyuan Fu ◽  
Shicheng Wang
Keyword(s):  
Target Recognition ◽  
Low Frequency ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Scale Transformation ◽  
Sar Image ◽  
Sar Images ◽  
Stationary Target ◽  
Multilevel Sampling ◽  
Process Method

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

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