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.

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 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.

Target Recognition in SAR Image by Joint Classification of Target Region and Shadow

2019 ◽  
Vol 12 (4) ◽  
pp. 347-354
Author(s):  
Yongpeng Tao ◽  
Yu Jing ◽  
Cong Xu
Keyword(s):  
Learning Algorithm ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Image ◽  
Stationary Target ◽  
Target Region ◽  
Feature Vectors ◽  
Joint Classification

Background: A synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed in this paper via the joint classification of the target region and shadow. Methods: The elliptical Fourier descriptors (EFDs) are used to describe the target region and shadow extracted from the original SAR image. In addition, the relative positions between the target region and shadow are represented by a constructed feature vector. The three feature vectors complement each other to provide more comprehensive descriptions of the target’s physical properties, e.g., sizes and shape. In the classification stage, the three feature vectors are jointly classified based on the joint sparse representation (JSR). JSR is a multi-task learning algorithm, which can not only represent each component properly but also exploit the inner correlations of different components. Finally, the target type is determined to the class with the minimum reconstruction error. Results: Experiments have been conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The proposed method achieves a high recognition accuracy of 96.86% for 10-class recognition problem under the standard operating condition (SOC). Moreover, robustness of the proposed method is also superior over the reference methods under the extended operating conditions (EOCs) like configuration variance, depression angle variance, and noise corruption. Conclusion: Therefore, the effectiveness and robustness of the proposed method can be quantitatively demonstrated by the experimental results.

SAR Target Recognition Based on Joint Representation of Multimode Representations

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Youchun Qiu
Keyword(s):  
Target Recognition ◽  
Decision Fusion ◽  
Reconstruction Error ◽  
Operating Conditions ◽  
Sar Image ◽  
Multiple Modes ◽  
Weighted Fusion ◽  
Mode Decomposition ◽  
Intrinsic Correlation ◽  
Target Characteristics

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).

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.

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.

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.

scholarly journals Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaohui Zhao ◽  
Yicheng Jiang ◽  
Tania Stathaki
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Synthetic Aperture ◽  
Stationary Target ◽  
Feature Sets ◽  
Radar Images ◽  
Feature Processing ◽  
Aperture Radar

A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses.

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