scholarly journals SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

2019 ◽  
Vol 11 (22) ◽  
pp. 2676 ◽  
Author(s):  
Meiting Yu ◽  
Sinong Quan ◽  
Gangyao Kuang ◽  
Shaojie Ni
Keyword(s):  
Target Recognition ◽  
Operating Conditions ◽  
Monogenic Signal ◽  
Sar Images ◽  
Stationary Target ◽  
Common Information ◽  
Training Samples ◽  
New Strategy ◽  
Dense Representation ◽  
Sar Target Recognition

Synthetic aperture radar (SAR) target recognition under extended operating conditions (EOCs) is a challenging problem due to the complex application environment, especially for insufficient target variations and corrupted SAR images in the training samples. This paper proposes a new strategy to solve these problems for target recognition. The SAR images are firstly characterized by multi-scale components of monogenic signal. The generated monogenic features are decomposed to learn a class dictionary and a shared dictionary, which represent the possible intraclass variations information and the common information, respectively. Moreover, a sparse representation of the class dictionary and a dense representation of the shared dictionary are jointly employed to represent a query sample for classification. The validity of the proposed strategy is demonstrated with multiple comparative experiments on moving and stationary target acquisition and recognition (MSTAR) database.

SAR Target Recognition via Joint Classification of Monogenic Components with Discrimination Analysis

2019 ◽  
Vol 23 (3) ◽  
pp. 414-420
Author(s):  
Zhenyu Zhang ◽  
Keyword(s):  
Classification Scheme ◽  
Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Stationary Target ◽  
Discrimination Analysis ◽  
Phase Amplitude ◽  
Joint Classification ◽  
Sar Target Recognition

This paper proposes a method using joint classification of monogenic components with discrimination analysis for target recognition in synthetic aperture radar (SAR) images. Three monogenic components, namely, phase, amplitude, and orientation, are extracted from the original image and classified by joint sparse representation for target recognition. Considering that the three components may have different discrimination capabilities for different operating conditions, the discrimination analysis is incorporated into the classification scheme. The components with low discriminability are not used in the joint classification. Afterwards, those discriminative components for a certain condition are classified to determine the target type. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) to evaluate the performance of the proposed method.

scholarly journals Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chenyu Li ◽  
Guohua Liu
Keyword(s):  
Bayesian Learning ◽  
Block Structure ◽  
Target Recognition ◽  
Recognition Performance ◽  
Test Sample ◽  
Synthetic Aperture ◽  
Sparse Bayesian Learning ◽  
Sar Images ◽  
Stationary Target ◽  
Sar Target Recognition

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

scholarly journals Discrimination and Correlation Analysis of Multiview SAR Images with Application to Target Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Chen ◽  
Peng Zhan ◽  
Luhui Cao ◽  
Xueqing Li
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Support Vector ◽  
Sar Images ◽  
Stationary Target ◽  
Weighting Strategy ◽  
Set Up ◽  
Sar Target Recognition

A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.

scholarly journals Target Recognition of Synthetic Aperture Radar Images by Updated Classifiers

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jingyu Li ◽  
Cungen Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Original Training ◽  
Stationary Target ◽  
Radar Images ◽  
Training Samples ◽  
Sar Target Recognition ◽  
Aperture Radar

For the problem of reliable decision in synthetic aperture radar (SAR) target recognition, a method based on updated classifiers is proposed. The convolutional neural network (CNN) and support vector machine (SVM) are used as basic classifiers to classify samples with unknown target labels. The two decisions are fused and the reliability of the fused decision is evaluated. The classified test samples with high reliabilities are added to the original training samples to update the classifiers. The updated classifiers have stronger classification abilities and the fused result of the two classifiers can obtain a more reliable decision. The proposed method is tested and verified based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The experimental results verify the effectiveness and robustness of the proposed method.

Combination of ELM and BEMD for Target Recognition of SAR Images

2021 ◽  
Vol 30 (13) ◽  
Author(s):  
Zhichao Liu ◽  
Baida Qu
Keyword(s):  
Feature Extraction ◽  
Embedded System ◽  
High Efficiency ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Intrinsic Mode Functions ◽  
Sar Images ◽  
Stationary Target

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

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 Target Recognition of SAR Images via Matching Attributed Scattering Centers with Binary Target Region

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3019 ◽  
Author(s):  
Jian Tan ◽  
Xiangtao Fan ◽  
Shenghua Wang ◽  
Yingchao Ren
Keyword(s):  
Target Recognition ◽  
Recognition Rate ◽  
Test Sample ◽  
Operating Conditions ◽  
Matching Problem ◽  
Sar Images ◽  
Traditional Methods ◽  
Stationary Target ◽  
Image Domain ◽  
Scattering Centers

A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.

scholarly journals Improving SAR Target Recognition Performance Using Multiple Preprocessing Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qinmin Ma
Keyword(s):  
Image Enhancement ◽  
Target Recognition ◽  
Recognition Performance ◽  
Sar Image ◽  
Monogenic Signal ◽  
Stationary Target ◽  
Target Segmentation ◽  
Target Characteristics ◽  
Subsequent Target ◽  
Sar Target Recognition

The synthetic aperture radar (SAR) image preprocessing techniques and their impact on target recognition performance are researched. The performance of SAR target recognition is improved by composing a variety of preprocessing techniques. The preprocessing techniques achieve the effects of suppressing background redundancy and enhancing target characteristics by processing the size and gray distribution of the original SAR image, thereby improving the subsequent target recognition performance. In this study, image cropping, target segmentation, and image enhancement algorithms are used to preprocess the original SAR image, and the target recognition performance is effectively improved by combining the above three preprocessing techniques. On the basis of image enhancement, the monogenic signal is used for feature extraction and then the sparse representation-based classification (SRC) is used to complete the decision. The experiments are conveyed on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results prove that the combination of multiple preprocessing techniques can effectively improve the SAR target recognition performance.

scholarly journals Target Recognition in SAR Images Based on Multiresolution Representations with 2D Canonical Correlation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaojing Tan ◽  
Ming Zou ◽  
Xiqin He
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Operating Conditions ◽  
Data Set ◽  
Stationary Target ◽  
Sar Target Recognition

This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution representations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated to be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is beneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of the multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively convey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens caused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed to classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works. The moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method. According to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of targets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration variance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison with some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.

scholarly journals Region Matching of SAR Images Using Blocks for Target Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chao Shan ◽  
Minggao Li ◽  
Zihao Chen ◽  
Lei Han
Keyword(s):  
Target Recognition ◽  
Reconstruction Error ◽  
Sar Images ◽  
Stationary Target ◽  
Time Frequency ◽  
Feature Vectors ◽  
Region Matching ◽  
Random Weight ◽  
Four Blocks ◽  
Sar Target Recognition

A synthetic aperture radar (SAR) target recognition method based on image blocking and matching is proposed. The test SAR image is first separated into four blocks, which are analyzed and matched separately. For each block, the monogenic signal is employed to describe its time-frequency distribution and local details with a feature vector. The sparse representation-based classification (SRC) is used to classify the four monogenic feature vectors and produce the reconstruction error vectors. Afterwards, a random weight matrix with a rich set of weight vectors is used to linearly fuse the feature vectors and all the results are analyzed in a statistical way. Finally, a decision value is designed based on the statistical analysis to determine the target label. The proposed method is tested on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results confirm the validity of the proposed method.

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