Name
Abstract | ||
|---|---|---|
Video synthetic aperture radar (VideoSAR) presents significant potential for improving the performance of information interpretation. Key frames represent the aspect-dependent electromagnetic energy, which frequently obscures other scattering physics dominated by specular returns. In this paper, we propose a vision-based background subtraction approach for capturing VideoSAR scattering key-frame information in simultaneously single-channel and single-pass configurations. The spatiotemporal key-frame extractor combines subaperture energy gradient with modified statistical and knowledge-based object tracker. It can robustly discriminate the scattering features of the alternation between transient persistence and disappearance. We evaluate the proposed method using several measured airborne data. Experimental results and performance comparison have demonstrated that the scattering key-frame extractor can achieve a high accuracy for VideoSAR summarization. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323096 | IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium |
Keywords | DocType | ISSN |
Video synthetic aperture radar,scattering key-frame,video summarization,computer vision | Conference | 2153-6996 |
ISBN | Citations | PageRank |
978-1-7281-6375-8 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Zhang | 1 | 163 | 25.25 |
| Lichao Mau | 2 | 0 | 0.34 |
| Daiyin Zhu | 3 | 0 | 0.34 |
| Xiao Xiang Zhu | 4 | 896 | 103.00 |