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.

scholarly journals Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2940
Author(s):  
Xinzheng Zhang ◽  
Zhiying Tan ◽  
Guo Liu ◽  
Hongqing Liu ◽  
Yijian Wang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sparse Learning ◽  
Target Classification ◽  
Stationary Target ◽  
Testing Sample ◽  
Training Samples ◽  
Global Aspect ◽  
Training Subset ◽  
Aperture Radar

In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector. By doing so, the local aspect dictionary pair is obtained. Finally, the class label of the testing sample is inferred by a use of the minimum reconstruction residual under the representation with the local aspect dictionary pair. Using the local aspect sector training subset rather than the global aspect training set reduces the interference of a large amount of unrelated training samples, which leads to a more discriminative local aspect dictionary pair for target classification. The experiments are conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, and the results demonstrate that the proposed approach is effective and superior to the state-of-the-art methods.

Automatic Target Recognition from Inverse Synthetic Aperture Radar Images

2017 ◽  
pp. 530-555
Author(s):  
Hari Kishan Kondaveeti ◽  
Valli Kumari Vatsavayi
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Classification Method ◽  
Synthetic Aperture ◽  
Inverse Synthetic Aperture Radar ◽  
Basic Principles ◽  
Radar Images ◽  
Detection And Identification ◽  
Aperture Radar

In this chapter, Inverse Synthetic Aperture Radar, a special type of active microwave synthetic aperture radar is introduced and its applications in military surveillance are presented. Then, the basic principles involved in data acquisition and image generation are explained. The issues and challenges involved in processing the ISAR images for autonomous target detection and identification are discussed later. The proposed classification method is explained and its accuracy is evaluated experimentally against the conventional classification method in the rest of the chapter.

Automatic Target Recognition From Inverse Synthetic Aperture Radar Images

2018 ◽  
pp. 2307-2332
Author(s):  
Hari Kishan Kondaveeti ◽  
Valli Kumari Vatsavayi
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Classification Method ◽  
Synthetic Aperture ◽  
Inverse Synthetic Aperture Radar ◽  
Basic Principles ◽  
Radar Images ◽  
Detection And Identification ◽  
Aperture Radar

In this chapter, Inverse Synthetic Aperture Radar, a special type of active microwave synthetic aperture radar is introduced and its applications in military surveillance are presented. Then, the basic principles involved in data acquisition and image generation are explained. The issues and challenges involved in processing the ISAR images for autonomous target detection and identification are discussed later. The proposed classification method is explained and its accuracy is evaluated experimentally against the conventional classification method in the rest of the chapter.

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.

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