Video SAR Moving Target Indication Using Deep Neural Network

Ding, Jian; Wen, Liwu; Zhong, Chao; Loffeld, Otmar

doi:10.1109/tgrs.2020.2980419

Cited by 65 publications

(22 citation statements)

References 28 publications

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“…Object detection and tracking 2D/3D Yes Yes Deep learning for moving target (shadow) tracking considering abnormal playback speed (it looks without a general tracking filter) Damini et al [46] 2013 Change detection 3D No No Practical tests have been done Li et al [47] 2019 Object detection 3D Yes No Suppressing false alarm in Video-SAR Ding et al [48] 2020 Object detection 2D Yes Yes Moving target indication using deep learning Huang et al [49] 2021…”

Section: D Yes Nomentioning

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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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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Section: D Yes Nomentioning

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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“…For a moving target, the higher the speed, the longer the theoretical length of its shadow. However, as the speed increases, the contrast between the shadow and background decreases due to the shorter occlusion time [10]. A slower target has a more distinct shadow, and the shadow intensity roughly varies with the occlusion time linearly.…”

Section: A Simulated Video Sar Datamentioning

confidence: 99%

“…more sensitive to its motion, and thus the Doppler energy of target usually deviates from its real position. As a result, shadows can be more easily observed compared to the traditional SAR system [10]. It is very difficult to directly detect the moving target in SAR images, especially when its Doppler energy is smeared and shifted, which may be outside the scene as an extreme case [11].…”

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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“…ConvLSTM network was proposed for precipitation nowcasting [36] for the first time. Recently, with the powerful capability of feature expression on multi-temporal image sequences or video, ConvLSTM network attracts lots of research interests in many applications, including rain removal [37,38], image classification [39,40], moving target indication [41], and Earth surface monitoring [42]. It is suggested that this model is theoretically appropriate for domain adaptation.…”

Section: Convolutional Long Short-term Memory Networkmentioning

confidence: 99%

Progressive Domain Adaptation for Change Detection Using Season-Varying Remote Sensing Images

et al. 2020

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The development of artificial intelligence technology has prompted an immense amount of researches on improving the performance of change detection approaches. Existing deep learning-driven methods generally regard changes as a specific type of land cover, and try to identify them relying on the powerful expression capabilities of neural networks. However, in practice, different types of land cover changes are generally influenced by environmental factors at different degrees. Furthermore, seasonal variation-induced spectral differences seriously interfere with those of real changes in different land cover types. All these problems pose great challenges for season-varying change detection because the real and seasonal variation-induced changes are technically difficult to separate by a single end-to-end model. In this paper, by embedding a convolutional long short-term memory (ConvLSTM) network into a conditional generative adversarial network (cGAN), we develop a novel method, named progressive domain adaptation (PDA), for change detection using season-varying remote sensing images. In our idea, two cascaded modules, progressive translation and group discrimination, are introduced to progressively translate pre-event images from their own domain to the post-event one, where their seasonal features are consistent and their intrinsic land cover distribution features are retained. By training this hybrid multi-model framework with certain reference change maps, the seasonal variation-induced changes between paired images are effectively suppressed, and meanwhile the natural and human activity-caused changes are greatly emphasized. Extensive experiments on two types of season-varying change detection datasets and a comparison with other state-of-the-art methods verify the effectiveness and competitiveness of our proposed PDA.

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Video SAR Moving Target Indication Using Deep Neural Network

Cited by 65 publications

References 28 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

Progressive Domain Adaptation for Change Detection Using Season-Varying Remote Sensing Images

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