A novel rotational invariants target recognition method for rotating motion blurred images

2017 ◽  
Author(s):  
Meiling Gong ◽  
Jinhui Lan ◽  
Mingwei Dong ◽  
Yiliang Zeng ◽  
Yuzhen Zhang
Keyword(s):  
Target Recognition ◽  
Recognition Method ◽  
Blurred Images ◽  
Rotational Invariants

The Cat's Eye Effect Target Recognition Method Based on Visual Attention

2019 ◽  
Vol 28 (5) ◽  
pp. 1080-1086 ◽  
Author(s):  
Xingbin Wang ◽  
Jun Zhang ◽  
Shuaihui Wang
Keyword(s):  
Visual Attention ◽  
Target Recognition ◽  
Recognition Method

scholarly journals A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23432-23453
Author(s):  
Wang Lu ◽  
Yasheng Zhang ◽  
Canbin Yin ◽  
Caiyong Lin ◽  
Can Xu ◽  
...  
Keyword(s):  
Target Recognition ◽  
Recognition Method ◽  
Image Satellite

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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