scholarly journals An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

2021 ◽  
Vol 13 (19) ◽  
pp. 3864
Author(s):  
Changjie Cao ◽  
Zongyong Cui ◽  
Zongjie Cao ◽  
Liying Wang ◽  
Jianyu Yang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Training Set ◽  
Stationary Target ◽  
Sample Set ◽  
Filtering Approach

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

scholarly journals TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1724
Author(s):  
Zilu Ying ◽  
Chen Xuan ◽  
Yikui Zhai ◽  
Bing Sun ◽  
Jingwen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Network Architecture ◽  
Learning Strategy ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Speckle Noise ◽  
Small Sample ◽  
Operating Conditions ◽  
Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

scholarly journals FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition

2021 ◽  
Vol 13 (17) ◽  
pp. 3493
Author(s):  
Jifang Pei ◽  
Zhiyong Wang ◽  
Xueping Sun ◽  
Weibo Huo ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Imaging System ◽  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Sar Images ◽  
Stationary Target ◽  
Single View ◽  
Learning Modules ◽  
Deep Feature Extraction ◽  
Classification Information

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

scholarly journals Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mengmeng Huang ◽  
Fang Liu ◽  
Xianfa Meng
Keyword(s):  
Target Recognition ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Training Data ◽  
Small Scale ◽  
Data Set ◽  
Shape Information ◽  
Target Shape ◽  
The One

Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set.

scholarly journals SAR ATR of Ground Vehicles Based on ESENet

2019 ◽  
Vol 11 (11) ◽  
pp. 1316 ◽  
Author(s):  
Li Wang ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Target Recognition ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Synthetic Aperture ◽  
Ground Vehicles ◽  
Feature Maps ◽  
Sar Images ◽  
Angle Variation ◽  
Stationary Target ◽  
Depression Angle

In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms that are based on the convolutional neural network (CNN) have achieved high recognition rates in the moving and stationary target acquisition and recognition (MSTAR) dataset. However, in a SAR ATR task, the feature maps with little information automatically learned by CNN will disturb the classifier. We design a new enhanced squeeze and excitation (enhanced-SE) module to solve this problem, and then propose a new SAR ATR network, i.e., the enhanced squeeze and excitation network (ESENet). When compared to the available CNN structures that are designed for SAR ATR, the ESENet can extract more effective features from SAR images and obtain better generalization performance. In the MSTAR dataset containing pure targets, the proposed method achieves a recognition rate of 97.32% and it exceeds the available CNN-based SAR ATR algorithms. Additionally, it has shown robustness to large depression angle variation, configuration variants, and version variants.

scholarly journals Multi-Block Mixed Sample Semi-Supervised Learning for SAR Target Recognition

2021 ◽  
Vol 13 (3) ◽  
pp. 361
Author(s):  
Ye Tian ◽  
Jianguo Sun ◽  
Pengyuan Qi ◽  
Guisheng Yin ◽  
Liguo Zhang
Keyword(s):  
Supervised Learning ◽  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Training Image ◽  
Data Sets ◽  
Data Set ◽  
Stationary Target ◽  
Mixing Method ◽  
Sar Target Recognition

In recent years, synthetic aperture radar (SAR) automatic target recognition has played a crucial role in multiple fields and has received widespread attention. Compared with optical image recognition with massive annotation data, lacking sufficient labeled images limits the performance of the SAR automatic target recognition (ATR) method based on deep learning. It is expensive and time-consuming to annotate the targets for SAR images, while it is difficult for unsupervised SAR target recognition to meet the actual needs. In this situation, we propose a semi-supervised sample mixing method for SAR target recognition, named multi-block mixed (MBM), which can effectively utilize the unlabeled samples. During the data preprocessing stage, a multi-block mixed method is used to interpolate a small part of the training image to generate new samples. Then, the new samples are used to improve the recognition accuracy of the model. To verify the effectiveness of the proposed method, experiments are carried out on the moving and stationary target acquisition and recognition (MSTAR) data set. The experimental results fully demonstrate that the proposed MBM semi-supervised learning method can effectively address the problem of annotation insufficiency in SAR data sets and can learn valuable information from unlabeled samples, thereby improving the recognition performance.

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 Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
David Vint ◽  
Matthew Anderson ◽  
Yuhao Yang ◽  
Christos Ilioudis ◽  
Gaetano Di Caterina ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Sar Images ◽  
Adversarial Networks ◽  
Technological Advances ◽  
Dataset Size ◽  
Resolution Imaging ◽  
High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

scholarly journals Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hongqiao Wang ◽  
Yanning Cai ◽  
Guangyuan Fu ◽  
Shicheng Wang
Keyword(s):  
Target Recognition ◽  
Low Frequency ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Scale Transformation ◽  
Sar Image ◽  
Sar Images ◽  
Stationary Target ◽  
Multilevel Sampling ◽  
Process Method

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.

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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