Automatic Target Recognition of SAR Images Using Collaborative Representation

Hu, Jinge

doi:10.1155/2022/3100028

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…Many template-based and machine learning-based SAR image target recognition algorithms [1][2][3][4][5][6] have been proposed and achieved certain effectiveness. However, these traditional SAR image target recognition algorithms have the following drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism

Tang,

Cui,

Feng

et al. 2024

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With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in existing SAR image target recognition focuses on spatial and channel information but lacks research on the relationship and recognition mechanism between spatial and channel information. In response to this issue, this article proposes a hybrid attention module and introduces a Mixed Attention (MA) mechanism module in the MobileNetV2 network. The proposed MA mechanism fully considers the comprehensive calculation of spatial attention (SPA), channel attention (CHA), and coordinated attention (CA). It can input feature maps for comprehensive weighting to enhance the features of the regions of interest, in order to improve the recognition rate of vehicle targets in SAR images.The superiority of our algorithm was verified through experiments on the MSTAR dataset.

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Section: Introductionmentioning

confidence: 99%

Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism

Tang,

Cui,

Feng

et al. 2024

Information

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“…The application of SAR images in the field of marine monitoring, geological exploration, etc. has been promoted [ 2 , 3 , 4 ]. In contrast to traditional SAR image interpretability methods, deep learning can extract inherent patterns from the large amounts of data.…”

Section: Introductionmentioning

confidence: 99%

The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models

Zheng

Hao

et al. 2023

Sensors

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Recently, attention has been paid to the convolutional neural network (CNN) based synthetic aperture radar (SAR) target recognition method. Because of its advantages of automatic feature extraction and the preservation of translation invariance, the recognition accuracies are stronger than traditional methods. However, similar to other deep learning models, CNN is a “black-box” model, whose working process is vague. It is difficult to locate the decision reasons. Because of this, we focus on the process analysis of a pre-trained CNN model. The role of the processing to feature extraction and final recognition decision is discussed. The discussed components of CNN models are convolution, activation function, and full connection. Here, the convolution processing can be deemed as image filtering. The activation function provides a nonlinear element of processing. Moreover, the fully connected layers can also further extract features. In the experiment, four classical CNN models, i.e., AlexNet, VGG16, GoogLeNet, and ResNet-50, are trained by public MSTAR data, which can realize ten-category SAR target recognition. These pre-trained CNN models are processing objects of the proposed process analysis method. After the analysis, the content of the SAR image target features concerned by these pre-trained CNN models is further clarified. In summary, we provide a paradigm to process the analysis of pre-trained CNN models used for SAR target recognition in this paper. To some degree, the adaptability of these models to SAR images is verified.

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“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”

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confidence: 99%

Retracted: Automatic Target Recognition of SAR Images Using Collaborative Representation

Intelligence and Neuroscience

2023

Computational Intelligence and Neuroscience

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Automatic Target Recognition of SAR Images Using Collaborative Representation

Cited by 4 publications

References 40 publications

Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism

Vehicle Target Recognition in SAR Images with Complex Scenes Based on Mixed Attention Mechanism

The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models

Retracted: Automatic Target Recognition of SAR Images Using Collaborative Representation

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