SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

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Multiple Feature Aggregation Using Convolutional Neural Networks for SAR Image-Based Automatic Target Recognition

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2865608 ◽

2018 ◽

Vol 15 (12) ◽

pp. 1882-1886 ◽

Cited By ~ 16

Author(s):

Jun Hoo Cho ◽

Chan Gook Park

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Target Recognition ◽

Automatic Target Recognition ◽

Sar Image ◽

Multiple Feature ◽

Feature Aggregation

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Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11020135 ◽

2019 ◽

Vol 11 (2) ◽

pp. 135 ◽

Cited By ~ 5

Author(s):

Xiaoran Shi ◽

Feng Zhou ◽

Shuang Yang ◽

Zijing Zhang ◽

Tao Su

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Target Recognition ◽

Super Resolution ◽

Automatic Target Recognition ◽

Synthetic Aperture ◽

Sar Image ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Feature Characterization

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

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

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180727110642 ◽

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.

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Automatic Target Recognition in High Resolution SAR Image Based on Electromagnetic Characteristics

Intelligent Computing in Signal Processing and Pattern Recognition - Lecture Notes in Control and Information Sciences ◽

10.1007/978-3-540-37258-5_17 ◽

2006 ◽

pp. 162-171 ◽

Cited By ~ 1

Author(s):

Wen-Ming Zhou ◽

Jian-She Song ◽

Jun Xu ◽

Yong-An Zheng

Keyword(s):

High Resolution ◽

Target Recognition ◽

Automatic Target Recognition ◽

Sar Image ◽

Electromagnetic Characteristics

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Convolutional Neural Network-based Automatic Target Recognition Algorithm in SAR Image

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2017.17.0100 ◽

2017 ◽

Vol 23 (8) ◽

pp. 644-649 ◽

Cited By ~ 1

Author(s):

Jun Hoo Cho ◽

Chang Ho Kang ◽

Chan Gook Park

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Target Recognition ◽

Automatic Target Recognition ◽

Recognition Algorithm ◽

Sar Image

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A SAR Image Target Recognition Approach via Novel SSF-Net Models

Computational Intelligence and Neuroscience ◽

10.1155/2020/8859172 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Wei Wang ◽

Chengwen Zhang ◽

Jinge Tian ◽

Jianping Ou ◽

Ji Li

Keyword(s):

High Resolution ◽

Target Recognition ◽

Computational Cost ◽

Automatic Target Recognition ◽

Sar Image ◽

Processing Efficiency ◽

High Resolution Radar ◽

Public Datasets ◽

Radar Automatic Target Recognition ◽

Fully Connected

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.

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