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Video synthetic aperture radar (VideoSAR) presents significant potential to enhance the performance of automatic information retrieval and interpretation in the dynamic region of interest (DROI). Key-frame extraction represents effective contents in processing massive video data. In this article, a novel computer vision-based background subtraction perspective is proposed for automatic VideoSAR scattering key-frame selection. A universal parameterization scattering key-frame model is firstly investigated, which serves to reveal the VideoSAR scattering key frames. Then, a spatiotemporal scattering extractor, called the subaperture energy gradient (SEG), together with a modified statistical and knowledge-based object tracker (MSAKBOT) is proposed to robustly discriminate the scattering key-frame state of the alternation between transient persistence and disappearance. The proposed SEG-MSAKBOT method presents a more comprehensive measurement of scattering key frames that is adaptive to the multitemporal video sequences effectively. Finally, experimental results and performance assessment conducted on two real airborne VideoSAR data with the coherent integration angles of 21° and 27° demonstrate that the proposed perspective is robust and reliable to capture scattering key frames and video content summarization with highly accurate descriptions in various DROIs.
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Zhang et al. (2019) studied this question.
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