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47 results on '"Quan, Sinong"'

3. Detection and Type Recognition of SAR Artificial Modulation Targets Based on Multi-Scale Amplitude-Phase Features.

Author

Meng, Weize, Cai, Zhihao, Fang, Fuping, Feng, Dejun, Wang, Jinrong, Xing, Shiqi, and Quan, Sinong

Subjects
SYNTHETIC aperture radar, INDEPENDENT sets, RATE setting
Abstract

With respect to the detection and recognition of an Artificial Modulation Target (AMT) with different modulated types, the state-of-the-art methods generally suffer the deficiencies of overfitting and insufficient generalization of existing neural network solutions. To address these problems, this paper proposes a multi-scale amplitude-phase feature discrimination method for AMTs in SAR images. First, a multi-type modulated AMT Dataset is generated (AMT Detection and Modulation Type Recognition Dataset, ADMTR Dataset), wherein the factors of jamming position, jamming-to-signal ratio (JSR), and the modulated parameter are considered to enhance the generalization. Second, a Multi-Input Multi-Output Fusion Wavelet Neural Network (MIMOFWTNN) is established, which not only uses the amplitude information of the scene but also adequately makes use of the phase and high-frequency information. This empowers us to detect the AMT in a higher dimensional feature space such that the type recognition can be implemented with more certainty. Analysis and discussions conducted on comparison experiments and ablation experiments demonstrate that the proposed network can achieve an average accuracy of 96.96% on the cross-validation set and a correct rate of 99.0% on the completely independent test set, which outperforms the compared methods. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Shadow-Based False Target Identification for SAR Images.

Author

Zhang, Haoyu, Quan, Sinong, Xing, Shiqi, Wang, Junpeng, Li, Yongzhen, and Wang, Ping

Subjects
*SYNTHETIC aperture radar, *RADAR interference, *IMAGE analysis, *ELECTRONIC countermeasures
Abstract

In radar electronic countermeasures, as the difference between jamming and targets continues to decrease, traditional methods that are implemented based on classical features are currently unable to meet the requirements of jamming detection. Compared with classical features such as texture, scale, and shape, shadow has better discernability and separability. In this paper, target shadow is investigated and applied to detect jamming in Synthetic Aperture Radar (SAR) images, and a SAR false target identification method based on shadow features is proposed. First, a difference image is generated by change detection, which can extract the shadow region in single-time SAR images. Then, a three-step differentiation condition is proposed, which can distinguish false targets from real targets. Simulated experimental results show that the proposed method can effectively extract the shadow region in SAR images and accurately distinguishreal and false targets. Furthermore, the potential of shadow in SAR image interpretation and electronic countermeasures is also demonstrated. [ABSTRACT FROM AUTHOR]

Published
2023
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39. A cascaded three-look network for aircraft detection in SAR images.

Author

Zhang, Linbin, Li, Chuyin, Zhao, Lingjun, Xiong, Boli, Quan, Sinong, and Kuang, Gangyao

Subjects
ARTIFICIAL neural networks, OBJECT recognition (Computer vision), SYNTHETIC aperture radar, RUNWAYS (Aeronautics), IMAGE analysis, RADARSAT satellites, OPTICAL images, REMOTE sensing
Abstract

Aircraft detection in synthetic aperture radar (SAR) images is a tough problem in remote sensing image interpretation, due to different scattering characteristics and scales of aircrafts as well as the influences of airport metal objects. Although deep convolutional neural networks perform well in object detection on optical images, the application of deep learning for SAR image aircraft detection has not been adequately studied. In this article, a cascaded three-look network is proposed to locate aircrafts in SAR images. The detection networks for the airport and aircrafts are modified separately in region proposal, which combines the superiorities of Faster R-CNN in object detection and residual units in feature extraction. The network contains three looks in noticing airport, detecting aircraft and extracting airfield runway. Three looks are designed not only to reduce the detection range and false alarms but also to elevate the detection precision. Experiments conducted on test data demonstrate that the proposed method can achieve 0.67 in F1 score, which outperforms the compared method. [ABSTRACT FROM AUTHOR]

Published
2020
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