scholarly journals A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3039 ◽  
Author(s):  
Jiaqi Shao ◽  
Changwen Qu ◽  
Jianwei Li ◽  
Shujuan Peng
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Visual Attention ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Sar Image ◽  
Extraction Ability ◽  
Data Imbalance ◽  
Recognition Efficiency

With the continuous development of the convolutional neural network (CNN) concept and other deep learning technologies, target recognition in Synthetic Aperture Radar (SAR) images has entered a new stage. At present, shallow CNNs with simple structure are mostly applied in SAR image target recognition, even though their feature extraction ability is limited to a large extent. What’s more, research on improving SAR image target recognition efficiency and imbalanced data processing is relatively scarce. Thus, a lightweight CNN model for target recognition in SAR image is designed in this paper. First, based on visual attention mechanism, the channel attention by-pass and spatial attention by-pass are introduced to the network to enhance the feature extraction ability. Then, the depthwise separable convolution is used to replace the standard convolution to reduce the computation cost and heighten the recognition efficiency. Finally, a new weighted distance measure loss function is introduced to weaken the adverse effect of data imbalance on the recognition accuracy of minority class. A series of recognition experiments based on two open data sets of MSTAR and OpenSARShip are implemented. Experimental results show that compared with four advanced networks recently proposed, our network can greatly diminish the model size and iteration time while guaranteeing the recognition accuracy, and it can effectively alleviate the adverse effects of data imbalance on recognition results.

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 A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

2018 ◽  
Vol 11 (6) ◽  
pp. 809-824 ◽  
Author(s):  
Fei Gao ◽  
Teng Huang ◽  
Jinping Sun ◽  
Jun Wang ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Deep Convolutional Neural Network ◽  
Sar Image

scholarly journals Underwater target recognition method based on convolution residual network

2019 ◽  
Vol 283 ◽  
pp. 04011
Author(s):  
Yuechao Chen ◽  
Shuanping Du ◽  
HengHeng Quan ◽  
Bin Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Deep Structure ◽  
Target Recognition ◽  
Complex Signal ◽  
Recognition Method ◽  
Residual Network ◽  
Time Frequency ◽  
Underwater Target

The underwater target radiated noises usually have characteristics of low signal to noise ratio, complex signal components and so on. Therefore the recognition is a difficult task and powerful recognition method must be applied to obtain good results. In this paper, a recognition method for underwater target radiated noise time-frequency image based on convolutional neural network with residual units is proposed. The principles and characteristics of the convolutional residual network are analyzed and three basic convolutional residual units are put forward. Then three convolutional residual network models with very deep structure are established based on basic convolutional residual units and some normal convolution layers. The number of the hidden layers is 50, 100 and 150 respectively and softmax algorithm is used as the top classifier. The wavelet transform is adopted to generate time-frequency images of the underwater target radiated noises with frequency band of 10~200Hz, thus ensuring the accuracy of local structure of the image, then the above three models can be used to recognize the images. The experimental data of two types of targets were processed. The results are as follows. As the number of training time increases, the training loss shows a convergence trend and the recognition accuracy of test data gradually increases to more than 90%. In addition, the top-level output has obvious separability. The final recognition accuracies of the three convolutional residual networks are all over 93% and higher than that of normal convolutional neural network with 5 layers. As the number of layers increases, the recognition accuracy of the convolutional residual network increases to a certain extent, illustrating the increase of layer number can improve the processing effect. The analysis results show that the convolution residual network can extract features with separability through deep structure and achieve effective underwater target recognition.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

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