An SAR Image Automatic Target Recognition Method Based on the Scattering Parameter Gaussian Mixture Model

Qin, Jikai; Liu, Zheng; Ran, Lei; Xie, Rong; Tang, Junkui; Zhu, Hongyu

doi:10.3390/rs15153800

Cited by 7 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Synthetic aperture radar (SAR) deception jamming technology is effective in concealing important military facilities and operational equipment [1][2][3], enabling covert military operations [4,5]. The SAR deception jamming technology has the advantage of low power requirements, making it a popular research topic [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

Lang,

Li,

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming.

show abstract

Section: Introductionmentioning

confidence: 99%

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

Lang,

Li,

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

Automatic target detection and recognition of military vehicles in synthetic aperture radar images is fostered by optimizing VGG-googLeNet with the giraffe kicking optimization algorithm

Shakin Banu,

Shahul Hameed

2024

SIViP

View full text Add to dashboard Cite

Synthetic aperture radar automatic target recognition based on cost‐sensitive awareness generative adversarial network for imbalanced data

Qin,

Liu,

Ran

et al. 2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

In military contexts, synthetic aperture radar (SAR) automatic target recognition (ATR) models frequently encounter the challenge of imbalanced data, resulting in a noticeable degradation in the recognition performance. Therefore, the authors propose a cost‐sensitive awareness generative adversarial network (CAGAN) model, aiming to improve the robustness of ATR models under imbalanced data. Firstly, the authors introduce a convolutional neural network (DCNN) to extract features. Then, the synthetic minority over‐sampling technique (SMOTE) is applied to achieve feature‐level balancing for the minority category. Finally, a CAGAN model is designed to perform the final classification task. In this process, the GAN‐based adversarial training mechanism enriches the diversity of training samples, making the ATR model more comprehensive in understanding different categories. In addition, the cost matrix increases the penalty for misclassification results and further improves the classification accuracy. Simultaneously, the cost‐sensitive awareness can accurately adjust the cost matrix through training data, thus reducing dependence on expert knowledge and improving the generalisation performance of the ATR model. This model is an end‐to‐end ATR scheme, which has been experimentally validated on the MSTAR and OpenSARship datasets. Compared to other methods, the proposed method exhibits strong robustness in dealing with various imbalanced scenarios and significant generalisation capability across different datasets.

show abstract

An SAR Image Automatic Target Recognition Method Based on the Scattering Parameter Gaussian Mixture Model

Cited by 7 publications

References 54 publications

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

Automatic target detection and recognition of military vehicles in synthetic aperture radar images is fostered by optimizing VGG-googLeNet with the giraffe kicking optimization algorithm

Synthetic aperture radar automatic target recognition based on cost‐sensitive awareness generative adversarial network for imbalanced data

Contact Info

Product

Resources

About