Processing video-SAR data with the fast backprojection method

Song, Xiaoshen; Yu, Weidong

doi:10.1109/taes.2016.150581

Cited by 51 publications

(19 citation statements)

References 14 publications

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“…In order to achieve both high framerate and azimuth resolution, VideoSAR system divides echo data into some continuous and overlapped segments and processes them to obtain an image sequence. The detailed imaging process can be referred to [13,14]. The shadow of moving target can be divided into two parts: non-cast shadow caused by the Doppler displacement of target energy and the cast shadow caused by the squint imaging of SAR sensor.…”

Section: Shadow Region Analysismentioning

confidence: 99%

Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information

Dong

et al. 2019

Remote Sensing

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VideoSAR (Video Synthetic Aperture Radar) technology provides an important mean for real-time and continuous earth observation, whereas the ever-changing scattering characteristics may destroy the accuracy of target motion perception and bring in massive false alarms subsequently. False alarms emerge easily in the edge region for its sharper variations of the scattering characteristics. Utilizing the gradient difference between the target shadow edge and other edge regions in the image, this letter proposes a VideoSAR false alarm reduction method based on gradient-weighted edge information. By considering the reasonable gradient and area of the overlapping edge region between changing region and background, this method could reduce the amount of false alarms ( P f a = 18 . 4 % ) and retain the correct shadow of moving target ( P d = 74 . 8 % ). Experiments on a real footage verify the excellent effect of the proposed method.

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Section: Shadow Region Analysismentioning

confidence: 99%

Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information

Dong

et al. 2019

Remote Sensing

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“…However, this method requires a large amount of calculation when analyzing motion, and the local minimum is not necessarily accurate [21,22]. Researchers select multiple key frames based on the significant changes between frames [23][24][25]. Relevant scholars calculate the energy and standard deviation characteristics of each subband after contourlet conversion to form a feature vector to represent the video frame, thereby extracting key frames [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Key Frame Extraction Algorithm of Motion Video Based on Priori

Zhong

Zhang

et al. 2020

IEEE Access

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This work was supported by China University of Geosciences (Beijing) undergraduate education quality improvement plan off campus practice base construction project, college students outdoor sports practice teaching base, project number: SJJD202006 ABSTRACT Key frame extraction technology is one of the core technologies of content-based video retrieval. For video types with complex content, various scenes, and rich actions, the performance of existing key frame extraction methods is not ideal. Based on the Visual Geometry Group (VGG), this paper proposes an image saliency extraction model assisted by deep prior information, and uses a large-scale data set for training on the server to obtain a trained model, and then integrates multiple features. The saliency extraction algorithm is combined with the image saliency extraction model assisted by deep prior information, and a saliency extraction algorithm based on multi-feature fusion and deep prior information is proposed. A new method for extracting key frames of motion video is introduced in detail. Taking into account that sports videos in real applications are susceptible to interference from various factors, resulting in poor picture quality, this article constructs a new visual attention model for moving targets in sports videos, which integrates images. The combination of multiple features of the bottom-level features and the skin color confidence map of the moving target overcomes the problem that a single feature cannot fully express the moving target. Since the processing object in this article is for the moving target in the video of the sports room, the extracted moving target can provide samples for video post-processing. The experimental results show that the proposed key frame extraction algorithm can quickly grasp the pedestrian information in the motion video and provide effective processing samples for the motion target for video post-processing. INDEX TERMS key frame extraction; deep prior information; zero-sample learning; motion video; visual attention model.

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“…It apperceives the regional information in the form of a dynamic video displaying and boosts the robustness of ground moving target indication, coherent change detection and three‐dimensional imaging etc. [5–9].…”

Section: Introductionmentioning

confidence: 99%