scholarly journals Bidimensional Empirical Mode Decomposition for SAR Image Feature Extraction With Application to Target Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 135720-135731 ◽  
Author(s):  
Ming Chang ◽  
Xuqun You ◽  
Zhengyang Cao
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Target Recognition ◽  
Image Feature ◽  
Sar Image ◽  
Image Feature Extraction ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

scholarly journals Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xinying Miao ◽  
Yunlong Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Target Recognition ◽  
Test Sample ◽  
Operating Conditions ◽  
Sar Image ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Standard Operating Condition ◽  
Bidimensional Empirical Mode Decomposition

A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

Overview of SAR Image Feature Extraction and Target Recognition

2021 ◽  
pp. 69-75
Author(s):  
Zhixu Wang ◽  
Zhihui Xin ◽  
Xiaoqiao Huang ◽  
Yu Sun ◽  
Jiayu Xuan
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Image Feature ◽  
Sar Image ◽  
Image Feature Extraction

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.

The Image Feature Extraction Algorithm Based on the DWT and the Improved 2DPCA

2014 ◽  
Vol 556-562 ◽  
pp. 5042-5045 ◽  
Author(s):  
Wu Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Target Recognition ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Two Dimension ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
New Feature ◽  
Pca Algorithm

The technology of 2DPCA is the feature extraction method proposed aiming at two-dimension image based on the traditional PCA algorithm. The paper proposed a improved weighting 2DPCA algorithm, combined with the two-dimension discrete DWT to handle the image, posing the new feature abstraction method, experiment improved that the new feature abstraction method can improve the target recognition efficiently compared with the original 2DPCA algorithm.

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