scholarly journals Target Recognition of Synthetic Aperture Radar Images Based on Two-Phase Sparse Representation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wen Li ◽  
Jun Yang ◽  
Yide Ma
Keyword(s):  
Synthetic Aperture Radar ◽  
Sparse Representation ◽  
Target Recognition ◽  
Test Sample ◽  
Superior Performance ◽  
Synthetic Aperture ◽  
Collaborative Representation ◽  
The Absolute ◽  
Sar Target Recognition ◽  
Aperture Radar

A synthetic aperture radar (SAR) target recognition method is proposed via linear representation over the global and local dictionaries. The collaborative representation is performed on the local dictionary, which comprises of training samples from a single class. Then, the reconstruction errors as for representing the test sample reflect the absolute representation capabilities of different training classes. Accordingly, the target label can be directly decided when one class achieves a notably lower reconstruction error than the others. Otherwise, several candidate classes with relatively low reconstruction errors are selected as the candidate classes to form the global dictionary, based on which the sparse representation-based classification (SRC) is performed. SRC also produces the reconstruction errors of the candidate classes, which reflect their relative representation capabilities for the test sample. As a comprehensive consideration, the reconstruction errors from the collaborative representation and SRC are fused for decision-making. Therefore, the proposed method could inherit the high efficiency of the collaborative representation. In addition, the selection of the candidate training classes also relieves the computational burden during SRC. By combining the absolute and relative representation capabilities, the final classification accuracy can also be improved. During the experimental evaluation, the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method under several different operating conditions. The proposed method is compared with some other SAR target recognition methods simultaneously. The results show the superior performance of the proposed method.

scholarly journals SAR Target Recognition Using Improved Sparse Representation with Local Reconstruction

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Li Ma
Keyword(s):  
Synthetic Aperture Radar ◽  
Sparse Representation ◽  
Target Recognition ◽  
Test Sample ◽  
Synthetic Aperture ◽  
Stationary Target ◽  
Training Class ◽  
Coefficient Vector ◽  
Sparse Coefficient ◽  
Sar Target Recognition

In order to handle the problem of synthetic aperture radar (SAR) target recognition, an improved sparse representation-based classification (SRC) is proposed. According to the sparse coefficient vector resulting from the global dictionary, the largest coefficient in each class is taken as the reference. Then, the surrounding neighborhoods of the sample with the largest coefficient are selected to construct the optimal local dictionary in each training class. Afterwards, the samples in the local dictionary are used to reconstruct the test sample to be identified. Finally, the decision is made according to the comparison of the reconstruction errors from different classes. In the experiments, the proposed method is verified based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method has performance advantages over existing methods, which demonstrates its effectiveness and robustness.

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.

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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