SAR target recognition method of MSTAR data set based on multi-feature fusion

Shi, Jing

doi:10.1109/bdicn55575.2022.00120

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Shi [10] developed a multi-feature fusion-based approach for SAR target recognition using the MSTAR dataset. The proposed method integrated Hu moment (image moments for shape analysis), Harris corner point (corner detection algorithm), and Gabor features (linear filters used for texture analysis) to capture target shape, corner features, and texture.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Deep Neural Networks for Military Target Classification using Synthetic Aperture Radar Images

Jacob,

Wall,

Sharif

2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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Target detection and classification in the military is an area that is very significant in modern battlefields. Using Synthetic Aperture Radar images for classifying targets adds to its significance, as these images are high-resolution images of the surface of the earth created using microwave radiation and they can be used anytime, anywhere, and in any weather conditions. A target classification system using deep learning to classify military vehicles from Synthetic Aperture Radar images is proposed in this study. The system uses a baseline Convolutional Neural Network to classify the images of military vehicles from the MSTAR dataset, achieving a baseline accuracy of 90%. Further transfer learning was applied to the system by using 5 different pre-trained networks, namely the InceptionV3, VGG16, VGG19, ResNet50, and MobileNet. These models were analysed and evaluated using 3 different evaluation metrics, the Confusion matrix, Classification report, and Mean Average Precision to discover the most accurate and efficient model for this task. The models VGG16 and MobileNet displayed the best performance on the dataset achieving accuracies of 98% and 97%, respectively. The ResNet50 model displayed the worst performance among the models, achieving an accuracy of 82%. While the other models, InceptionV3 and VGG19, achieved accuracies of 92% and 96% respectively.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Deep Neural Networks for Military Target Classification using Synthetic Aperture Radar Images

Jacob,

Wall,

Sharif

2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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“…A single feature only portrays the target characteristics from one aspect, which makes it difficult to describe all the information embedded in the polarization target. The application of multi-feature fusion recognition methods allows for the comprehensive exploitation and utilization of diverse information contained in multipolarization SAR data, effectively solving the problem of insufficient robustness of a single feature in complex scenarios [8][9][10]. Based on human perception and experience accumulation, researchers have designed many distinctive features from the intensity map of PolSAR targets, which generally have specific physical meanings.…”

Section: Introductionmentioning

confidence: 99%

MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition

Zhang

et al. 2023

Remote Sensing

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The distinctive polarization information of polarimetric SAR (PolSAR) has been widely applied to terrain classification but is rarely used for PolSAR target recognition. The target recognition strategies built upon multi-feature have gained favor among researchers due to their ability to provide diverse classification information. The paper introduces a robust multi-feature cross-fusion approach, i.e., a multi-feature dual-stage cross manifold attention network, namely, MF-DCMANet, which essentially relies on the complementary information between different features to enhance the representation ability of targets. In the first-stage process, a Cross-Feature-Network (CFN) module is proposed to mine the middle-level semantic information of monogenic features and polarization features extracted from the PolSAR target. In the second-stage process, a Cross-Manifold-Attention (CMA) transformer is proposed, which takes the input features represented on the Grassmann manifold to mine the nonlinear relationship between features so that rich and fine-grained features can be captured to compute attention weight. Furthermore, a local window is used instead of the global window in the attention mechanism to improve the local feature representation capabilities and reduce the computation. The proposed MF-DCMANet achieves competitive performance on the GOTCHA dataset, with a recognition accuracy of 99.75%. Furthermore, it maintains a high accuracy rate in the few-shot recognition and open-set recognition scenarios, outperforming the current state-of-the-art method by about 2%.

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Incorporation of the Self-attention Mechanism into Convolutional Neural Networks for the Target Recognition

Mishra,

Gupta,

Tanwar

2024

Lecture Notes in Networks and Systems

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SAR target recognition method of MSTAR data set based on multi-feature fusion

Cited by 6 publications

References 5 publications

Analysis of Deep Neural Networks for Military Target Classification using Synthetic Aperture Radar Images

Analysis of Deep Neural Networks for Military Target Classification using Synthetic Aperture Radar Images

MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition

Incorporation of the Self-attention Mechanism into Convolutional Neural Networks for the Target Recognition

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