Moving Target Velocity Estimation of Video SAR based on Shadow Detection

Shang, Shuo; Wu, Fangming; Zhou, Yang; Liu, Zhenhua

doi:10.1109/csrswtc50769.2020.9372520

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In this way, the multilook shadow is also complex. Its calculation formula can be derived from Equation (5) and expressed as…”

Section: Modeling Of Sar Image Shadow Multilooked In Complex Domainmentioning

confidence: 99%

“…The positive effect is that shadows can provide image relief, which is helpful for the understanding of SAR imagery. Additionally, shadows can embody the shapes and positions of targets, which makes the shadows applicable to target recognition [1][2][3], moving target detection [4][5][6][7], impervious surface extraction [8,9], and deforestation detection [10]. The negative effect is that shadows often cause a lack of echoes from ground targets within the shadow areas.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Modeling of Shadows in SAR Imagery

Luo,

Zhang,

Bao

et al. 2023

Mathematics

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Shadows are a special distortion in synthetic aperture radar (SAR) imaging. They often hamper proper image understanding and target recognition but also offer useful information, and therefore, the statistical modeling of SAR image shadows is imperative. In this endeavor, we systematically deduced the statistical models of shadows in multimodal SAR images, including single-look intensity and amplitude images and multilook intensity and amplitude images in a real domain and complex domain, respectively. In particular, for the filtered SAR image shadow, we introduced the generalized extreme value (GEV) distribution to characterize its statistics. We carried out an experiment on shadows in a real SAR image and conducted chi-square goodness-of-fit tests on the deduced models. Furthermore, we compared the deduced statistical models of shadows with state-of-the-art statistical models of SAR imagery. Finally, suggestions are given for selecting the optimal statistical model of shadows in multimodal SAR images.

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“…In this way, the multilook shadow is also complex. Its calculation formula can be derived from Equation (5) and expressed as…”

Section: Modeling Of Sar Image Shadow Multilooked In Complex Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Modeling of Shadows in SAR Imagery

Luo,

Zhang,

Bao

et al. 2023

Mathematics

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Ground Moving Targets Velocity Estimation based on Shadow and Target Detection

Shang¹,

Liu²,

Wu³

et al. 2021

2021 CIE International Conference on Radar (Radar)

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ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR

Bao

Zhang

2022

Remote Sensing

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Most existing SAR moving target shadow detectors not only tend to generate missed detections because of their limited feature extraction capacity among complex scenes, but also tend to bring about numerous perishing false alarms due to their poor foreground–background discrimination capacity. Therefore, to solve these problems, this paper proposes a novel deep learning network called “ShadowDeNet” for better shadow detection of moving ground targets on video synthetic aperture radar (SAR) images. It utilizes five major tools to guarantee its superior detection performance, i.e., (1) histogram equalization shadow enhancement (HESE) for enhancing shadow saliency to facilitate feature extraction, (2) transformer self-attention mechanism (TSAM) for focusing on regions of interests to suppress clutter interferences, (3) shape deformation adaptive learning (SDAL) for learning moving target deformed shadows to conquer motion speed variations, (4) semantic-guided anchor-adaptive learning (SGAAL) for generating optimized anchors to match shadow location and shape, and (5) online hard-example mining (OHEM) for selecting typical difficult negative samples to improve background discrimination capacity. We conduct extensive ablation studies to confirm the effectiveness of the above each contribution. We perform experiments on the public Sandia National Laboratories (SNL) video SAR data. Experimental results reveal the state-of-the-art performance of ShadowDeNet, with a 66.01% best f1 accuracy, in contrast to the other five competitive methods. Specifically, ShadowDeNet is superior to the experimental baseline Faster R-CNN by a 9.00% f1 accuracy, and superior to the existing first-best model by a 4.96% f1 accuracy. Furthermore, ShadowDeNet merely sacrifices a slight detection speed in an acceptable range.

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Moving Target Velocity Estimation of Video SAR based on Shadow Detection

Cited by 3 publications

References 4 publications

Statistical Modeling of Shadows in SAR Imagery

Statistical Modeling of Shadows in SAR Imagery

Ground Moving Targets Velocity Estimation based on Shadow and Target Detection

ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR

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