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 Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liqun Yu ◽  
Lu Wang ◽  
Yongxing Xu
Keyword(s):  
Target Recognition ◽  
Nonnegative Matrix ◽  
Decision Fusion ◽  
Reconstruction Error ◽  
Operating Conditions ◽  
Stationary Target ◽  
Noise Interference ◽  
Adaptive Weighting ◽  
Joint Classification ◽  
Sar Target Recognition

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

scholarly journals Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 701 ◽  
Author(s):  
Beige Ye ◽  
Taorong Qiu ◽  
Xiaoming Bai ◽  
Ping Liu
Keyword(s):  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Sample Entropy ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
State Recognition ◽  
Driving Fatigue

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

scholarly journals A Multiview SAR Target Recognition Method Using Inner Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lei Lei ◽  
Dongen Guo ◽  
Zhihui Feng
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Image Correlation ◽  
Multiple Views ◽  
Recognition Method ◽  
Sar Images ◽  
Weighting Method ◽  
Multiview Images ◽  
Correlation Information ◽  
Sar Target Recognition

This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.

scholarly journals A SAR Target Recognition Method via Combination of Multilevel Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhua Wang ◽  
Yuan Jiang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Recognition Method ◽  
Sar Images ◽  
Noise Interference ◽  
Feature Sets ◽  
Weighted Fusion ◽  
Standard Operating Condition ◽  
Sar Target Recognition

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

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