scholarly journals Improving SAR Target Recognition Performance Using Multiple Preprocessing Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qinmin Ma
Keyword(s):  
Image Enhancement ◽  
Target Recognition ◽  
Recognition Performance ◽  
Sar Image ◽  
Monogenic Signal ◽  
Stationary Target ◽  
Target Segmentation ◽  
Target Characteristics ◽  
Subsequent Target ◽  
Sar Target Recognition

The synthetic aperture radar (SAR) image preprocessing techniques and their impact on target recognition performance are researched. The performance of SAR target recognition is improved by composing a variety of preprocessing techniques. The preprocessing techniques achieve the effects of suppressing background redundancy and enhancing target characteristics by processing the size and gray distribution of the original SAR image, thereby improving the subsequent target recognition performance. In this study, image cropping, target segmentation, and image enhancement algorithms are used to preprocess the original SAR image, and the target recognition performance is effectively improved by combining the above three preprocessing techniques. On the basis of image enhancement, the monogenic signal is used for feature extraction and then the sparse representation-based classification (SRC) is used to complete the decision. The experiments are conveyed on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results prove that the combination of multiple preprocessing techniques can effectively improve the SAR target recognition performance.

scholarly journals Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chenyu Li ◽  
Guohua Liu
Keyword(s):  
Bayesian Learning ◽  
Block Structure ◽  
Target Recognition ◽  
Recognition Performance ◽  
Test Sample ◽  
Synthetic Aperture ◽  
Sparse Bayesian Learning ◽  
Sar Images ◽  
Stationary Target ◽  
Sar Target Recognition

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

scholarly journals Discrimination and Correlation Analysis of Multiview SAR Images with Application to Target Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Chen ◽  
Peng Zhan ◽  
Luhui Cao ◽  
Xueqing Li
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Support Vector ◽  
Sar Images ◽  
Stationary Target ◽  
Weighting Strategy ◽  
Set Up ◽  
Sar Target Recognition

A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.

scholarly journals SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

2019 ◽  
Vol 11 (22) ◽  
pp. 2676 ◽  
Author(s):  
Meiting Yu ◽  
Sinong Quan ◽  
Gangyao Kuang ◽  
Shaojie Ni
Keyword(s):  
Target Recognition ◽  
Operating Conditions ◽  
Monogenic Signal ◽  
Sar Images ◽  
Stationary Target ◽  
Common Information ◽  
Training Samples ◽  
New Strategy ◽  
Dense Representation ◽  
Sar Target Recognition

Synthetic aperture radar (SAR) target recognition under extended operating conditions (EOCs) is a challenging problem due to the complex application environment, especially for insufficient target variations and corrupted SAR images in the training samples. This paper proposes a new strategy to solve these problems for target recognition. The SAR images are firstly characterized by multi-scale components of monogenic signal. The generated monogenic features are decomposed to learn a class dictionary and a shared dictionary, which represent the possible intraclass variations information and the common information, respectively. Moreover, a sparse representation of the class dictionary and a dense representation of the shared dictionary are jointly employed to represent a query sample for classification. The validity of the proposed strategy is demonstrated with multiple comparative experiments on moving and stationary target acquisition and recognition (MSTAR) database.

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 Adversarial Self-Supervised Learning for Robust SAR Target Recognition

2021 ◽  
Vol 13 (20) ◽  
pp. 4158
Author(s):  
Yanjie Xu ◽  
Hao Sun ◽  
Jin Chen ◽  
Lin Lei ◽  
Kefeng Ji ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Target Recognition ◽  
Recognition Performance ◽  
Evaluation Framework ◽  
Great Success ◽  
Sar Image ◽  
Class Labels ◽  
Adversarial Example ◽  
The Military ◽  
Sar Target Recognition

Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years. Yet, the adversarial robustness of these models has received far less academic attention in the remote sensing community. In this article, we first present a comprehensive adversarial robustness evaluation framework for DNN-based SAR target recognition. Both data-oriented metrics and model-oriented metrics have been used to fully assess the recognition performance under adversarial scenarios. Adversarial training is currently one of the most successful methods to improve the adversarial robustness of DNN models. However, it requires class labels to generate adversarial attacks and suffers significant accuracy dropping on testing data. To address these problems, we introduced adversarial self-supervised learning into SAR target recognition for the first time and proposed a novel unsupervised adversarial contrastive learning-based defense method. Specifically, we utilize a contrastive learning framework to train a robust DNN with unlabeled data, which aims to maximize the similarity of representations between a random augmentation of a SAR image and its unsupervised adversarial example. Extensive experiments on two SAR image datasets demonstrate that defenses based on adversarial self-supervised learning can obtain comparable robust accuracy over state-of-the-art supervised adversarial learning methods.

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 A SAR Target Recognition Method via Combination of Multilevel Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhua Wang ◽  
Yuan Jiang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Recognition Method ◽  
Sar Images ◽  
Noise Interference ◽  
Feature Sets ◽  
Weighted Fusion ◽  
Standard Operating Condition ◽  
Sar Target Recognition

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

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.

Image Quality and Target Recognition

1967 ◽  
Vol 9 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Corwin A. Bennett ◽  
Samuel H. Winterstein ◽  
Robert E. Kent
Keyword(s):  
Grain Size ◽  
Image Quality ◽  
Modulation Transfer Function ◽  
Target Recognition ◽  
Recognition Performance ◽  
Aerial Photographs ◽  
Modulation Transfer ◽  
Task Definition ◽  
Subject Selection ◽  
Target Characteristics

The terminology and literature in the area of image quality and target recognition are reviewed. An experiment in which subjects recognized strategic and tactical targets in aerial photographs with controlled image degradations is described. Some findings are: Recognition performance is only moderate for representative conditions. There are wide differences among target types in the recognizability. Knowledge of a target's presence (briefing) greatly aids recognition. Better resolution means better performance. Enlarging the image such that a line of resolution subtends more than three minutes of arc hinders recognition. Grain size should be kept below 20 seconds of arc. It is suggested that the eventual application of the modulation transfer function approach to measurement of image quality and target characteristics will enable a quantitative subsuming of various quality-size relationships. More attention needs to be paid in recognition research to suitable task definition, target description, and subject selection.

scholarly journals Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

2021 ◽  
Vol 13 (20) ◽  
pp. 4021
Author(s):  
Lan Du ◽  
Lu Li ◽  
Yuchen Guo ◽  
Yan Wang ◽  
Ke Ren ◽  
...  
Keyword(s):  
Target Recognition ◽  
Synthetic Aperture ◽  
Radar Target ◽  
Sar Image ◽  
Sar Images ◽  
Radar Target Recognition ◽  
Probabilistic Distribution ◽  
Extraction Stage ◽  
Different Characteristics ◽  
Sar Target Recognition

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.

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