scholarly journals SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
XiuXia Ji ◽  
Yinan Sun
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Sar Image ◽  
Recognition Method ◽  
Average Correlation ◽  
Monogenic Signal ◽  
New Approach ◽  
Training Samples ◽  
Overcomplete Dictionary ◽  
Feature Information

It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

Sar Image Target Recognition Method Based On Sparse Representation Of Local Dictionary

2021 ◽  
pp. 104070
Author(s):  
Han Hongliang ◽  
Bai Yonglei ◽  
Lu Wei ◽  
Feng Fan ◽  
Wang Jianhua
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Sar Image ◽  
Recognition Method

Classification via Sparse Representation of Steerable Wavelet Frames on Grassmann Manifold: Application to Target Recognition in SAR Image

2017 ◽  
Vol 26 (6) ◽  
pp. 2892-2904 ◽  
Author(s):  
Ganggang Dong ◽  
Gangyao Kuang ◽  
Na Wang ◽  
Wei Wang
Keyword(s):  
Sparse Representation ◽  
Grassmann Manifold ◽  
Target Recognition ◽  
Sar Image ◽  
Wavelet Frames

scholarly journals Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Huijie Ding ◽  
Arthur K. L. Lin
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Target Recognition ◽  
Image Feature ◽  
Sar Image ◽  
Translation Invariant ◽  
Shearlet Transform ◽  
Machine Learning Classification ◽  
Joint Sparse Representation ◽  
Multilevel Representations

Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition. NSST was used to decompose an SAR image into multilevel representations. These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target. During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations. The joint sparse representation could represent individual components independently while considering the inner correlations between different components. Therefore, the precision of joint representation could be enhanced. Finally, the target label of the test sample was determined according to the overall reconstruction error. Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.

Weight Optimization for Multi-Task Sparse Representation in Sar Image Target Recognition

2019 ◽  
Author(s):  
Zhi Zhou ◽  
Zongjie Cao ◽  
Yalan Zhang ◽  
Yiming Pi ◽  
Nengyuan Liu
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Sar Image ◽  
Weight Optimization

scholarly journals A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhengwu Lu ◽  
Guosong Jiang ◽  
Yurong Guan ◽  
Qingdong Wang ◽  
Jianbo Wu
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Decision Fusion ◽  
Operating Conditions ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
Multiple Features ◽  
Decision Variables ◽  
Sar Target Recognition

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.

scholarly journals Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4333
Author(s):  
Pengfei Zhao ◽  
Lijia Huang ◽  
Yu Xin ◽  
Jiayi Guo ◽  
Zongxu Pan
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Recognition Method ◽  
Training Samples ◽  
Model Training ◽  
Sar Target Recognition

At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.

scholarly journals Image Target Recognition via Mixed Feature-Based Joint Sparse Representation

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xin Wang ◽  
Can Tang ◽  
Ji Li ◽  
Peng Zhang ◽  
Wei Wang
Keyword(s):  
Sparse Representation ◽  
Feature Vector ◽  
Target Recognition ◽  
Training Sample ◽  
Gabor Wavelet ◽  
Illumination Variation ◽  
Training Samples ◽  
Mixed Features ◽  
Joint Sparse Representation ◽  
Feature Dictionary

An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural network (CNN) are used to extract the Gabor wavelet features and deep features of training samples and test samples, respectively. Then, the contribution weights of the Gabor wavelet feature vector and the deep feature vector are calculated. After adaptive weighted reconstruction, we can form the mixed features and obtain the training sample feature set and test sample feature set. Aiming at the high-dimensional problem of mixed features, we use principal component analysis (PCA) to reduce the dimensions. Lastly, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on joint feature dictionary, the sparse representation based classifier (SRC) is used to recognize the targets. The experiments on different datasets show that this approach is superior to some other advanced methods.

SAR Image Target Recognition Method Combining Multi-Resolution Representation and Complex Domain CNN

2020 ◽  
Vol 57 (24) ◽  
pp. 241007
Author(s):  
乔良才 Qiao Liangcai
Keyword(s):  
Target Recognition ◽  
Complex Domain ◽  
Sar Image ◽  
Recognition Method
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