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

Abstract

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.

A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers

“…For the different types of features extracted from the same SAR image, they have a certain inherent correlation. For this reason, this study uses JSR to jointly represent them, thereby improving the overall accuracy [5,15,26]. e three monogenic feature vectors obtained from the test sample y are denoted as [y (1) y (2) y (3) ].…”

“…e sparse representation classification-based classification (SRC) was employed for SAR target recognition in [12][13][14][15][16][17][18][19][20][21][22]. With the development of deep learning in recent years, the convolutional neural network (CNN) has gradually become a hot tool in SAR target recognition, and a number of representative methods have emerged [23][24][25][26][27][28][29][30]. Similarly, classifier fusion is also used and verified in SAR target recognition.…”

Target Recognition of SAR Images Based on Azimuthal Constraint Reconstruction

“…e sparse representation-based classification (SRC) (including the modified ones) operated as the classifier in [22,[24][25][26][27][28]. For the unorder scattering centers, the present classifiers can hardly be directly used.…”

SAR Target Recognition Based on Joint Representation of Multimode Representations

“…In [16][17][18], several matching schemes based on scattering centers were developed and applied. In the classification stage, the corresponding decision-making mechanism is mainly designed by using mature classifiers or according to the characteristics of the features, including K-nearest neighbor (KNN) [8], support vector machine (SVM) [19][20][21], sparse representation-based classification (SRC) [21][22][23][24][25][26], and convolutional neural network (CNN) [27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”