A Video-SAR Imaging Technique for Aspect-Dependent Scattering in Wide Angle

Hu, Ruizhi; Min, Rui; Pi, Yiming

doi:10.1109/jsen.2017.2698161

Cited by 48 publications

(16 citation statements)

References 34 publications

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“…Synthetic-aperture radar (SAR) has proved to be an effective microwave remote sensing technique for high-resolution imaging. SAR video, a sequence of SAR images indexed on subapertures, is a natural, convenient, and revealing representation that captures anisotropic behaviour of aspect-dependent scattering [1][2][3][4] in wide-angle SAR imaging, including threedimensional [5] and circular SAR [6].…”

Section: Introductionmentioning

confidence: 99%

Azimuth apodization for autoregressive synthetic‐aperture radar imaging

Zhang

Jing

2021

IET Radar Sonar & Navi

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Autoregressive backprojection (ARBP) approximates the procedure of forming syntheticaperture radar (SAR) sequential images as an autoregressive (AR) process, avoiding redundant computation between adjacent images. Azimuth apodization for ARBP entails designing the aforementioned AR process, including AR order selection and AR model parameter estimation. However, the conventional azimuth apodization method for ARBP provides only empirical means to conduct such a procedure. Moreover, it cannot address the ringing effect from the infinite impulse response of the AR model, and consequently, other unwanted azimuth pulses will degrade the SAR images. This study discusses azimuth apodization for ARBP and proposes a scheme that includes methods for AR model order selection and parameter estimation. Firstly, matrix rank-k approximation is adopted for AR model order selection. Then, a revised azimuth apodization procedure for parameter estimation is discussed. It introduces a penalty factor to render the ringing effect and model estimation error controllable. Moreover, experiments based on an open data set validate the proposed ARBP azimuth apodization scheme. In this way, the proposed azimuth apodization obtains a balance between computational complexity and imaging quality for ARBP.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Introductionmentioning

confidence: 99%

Azimuth apodization for autoregressive synthetic‐aperture radar imaging

Zhang

Jing

2021

IET Radar Sonar & Navi

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“…With the further development of Internet technology and multimedia technology, as well as the popularization of digital devices and large-capacity storage devices, a large amount of video information is generated every day [1,2]. In daily life, humans mainly obtain information through the visual system.…”

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%

“…Also the main imaging processing ideas of VideoSAR frame sequence, for most occasions, are based on the mature imaging methods, i.e. multi‐subaperture [3–6] and multi‐block formation [7].…”

Section: Introductionmentioning

confidence: 99%