Moving Target Shadow Detection and Global Background Reconstruction for VideoSAR Based on Single-Frame Imagery

Liu, Zhongkang; An, Daoxiang; Huang, Xianglin

doi:10.1109/access.2019.2907146

Cited by 31 publications

(12 citation statements)

References 24 publications

(29 reference statements)

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“…Video-SAR despeckling without considering coding artifacts Liu et al [42] 2019 Object detection 2D Yes No Shadow detection filter Liao and Zhu [43] 2016…”

Section: D Yes Nomentioning

confidence: 99%

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Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

Khosravi

Samadi

2022

Tsinghua Sci. Technol.

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This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar (Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing.Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night (regardless of light sources) in all weathers. First, an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed.Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle (UAV) platforms, Internet of Multimedia Things (IoMT), Video-SAR data processing issues, and real-world applications are investigated.

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“…Video-SAR despeckling without considering coding artifacts Liu et al [42] 2019 Object detection 2D Yes No Shadow detection filter Liao and Zhu [43] 2016…”

Section: D Yes Nomentioning

confidence: 99%

“…As final point, there is a need to design stronger 2D processing techniques for the situation, such that more information around 2D and 3D Video-SAR data processing can be found in Refs. [1,[11][12][13][14][41][42][43]. 4.1.5 Machine intelligence: Detection, recognition, and tracking…”

Section: Single-and Multi-frame Computing Architecturementioning

confidence: 99%

Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

Khosravi

Samadi

2022

Tsinghua Sci. Technol.

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“…The current shadow-based detection methods are mainly based on classical image processing, including single-frame [13] and multi-frame processing [14]- [17]. Shadow detection based on classical image processing has received much attentions because of its independency of the number of channels.…”

Section: Introductionmentioning

confidence: 99%

“…Shadow detection based on classical image processing has received much attentions because of its independency of the number of channels. Liu [13] presented a local feature analysis method for shadow detection in a single-frame video SAR image. On the other hand, video SAR can provide continuous moving target shadows.…”

Section: Introductionmentioning

confidence: 99%

Video SAR Moving Target Detection Using Dual Faster R-CNN

Wen

Ding

Loffeld

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Video synthetic aperture radar (SAR) has shown great potentials in detection and tracking of slow ground moving targets. The classical shadow-aided detection was applied in video SAR, and most recently, the deep learning approach has been developed for shadow-aided moving target detection. This paper presents a joint moving target detection approach for video SAR using a dual faster region-based convolutional neural network (Faster R-CNN), which algorithmically combines the shadow detection in the SAR image and the Doppler energy detection in the Range-Doppler (RD) spectrum domain, and this new approach can suppress false alarm sufficiently. Video SAR image and its corresponding low resolution RD spectrum are fed into the developed dual Faster R-CNN. A correct detection can be achieved if the shadow of a moving target and its Doppler energy are simultaneously detected by paired region proposals which are obtained by sharing the region proposals of two independent region proposal networks (RPNs). Therefore, the performance of moving target detection can be significantly improved by using diverse features in different domains. This proposed approach has been verified by both the simulated and real video SAR data. Compared to other classical methods, our approach exhibits great detection performance in terms of fewer false alarms and acceptable missing alarms.

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“…In [17], a shadow-based low-RCS MTD method for the Ka band was proposed based on the geometric characteristics of the moving shadow. The authors of [18] presented a method of local feature analysis based on single-frame images that can accurately detect the shadow of a moving target. In [19], a dual fast region-based convolutional neural network for SAR images in the range Doppler (RD) spectrum domain was proposed to detect a moving target on the ground, but these algorithms have high requirements for training data [20] and insufficient generalization ability.…”

Section: Introductionmentioning

confidence: 99%

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

Chen

et al. 2021

Remote Sensing

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Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.

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Moving Target Shadow Detection and Global Background Reconstruction for VideoSAR Based on Single-Frame Imagery

Cited by 31 publications

References 24 publications

Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

Video SAR Moving Target Detection Using Dual Faster R-CNN

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

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