TY - JOUR
T1 - Moving Vehicle Detection Based on RPCA Using Multisquint Spaceborne SAR Images
AU - Li, Yulun
AU - Li, Chunsheng
AU - Yang, Wei
AU - Wang, Yamin
AU - Li, Yihan
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - In this letter, a moving vehicle detection method is proposed based on spaceborne synthetic aperture radar images, generated by antenna azimuth steering with different azimuth angles and time lags. First, the multisquint geometry and sequential image model are established, and the variation from stationary scatters over sequence is addressed, which interferes the indication of moving signals significantly. Then, the robust principal component analysis (RPCA) is employed in the image domain as the predetection step. Next, with some spatial filters and thresholding estimators, the moving target signature is exploited to further discriminate the residual false detection. Finally, the proposed method is verified by both simulated and real images based on TerraSAR-X data. Results reveal that the clutter suppression can be effectively achieved.
AB - In this letter, a moving vehicle detection method is proposed based on spaceborne synthetic aperture radar images, generated by antenna azimuth steering with different azimuth angles and time lags. First, the multisquint geometry and sequential image model are established, and the variation from stationary scatters over sequence is addressed, which interferes the indication of moving signals significantly. Then, the robust principal component analysis (RPCA) is employed in the image domain as the predetection step. Next, with some spatial filters and thresholding estimators, the moving target signature is exploited to further discriminate the residual false detection. Finally, the proposed method is verified by both simulated and real images based on TerraSAR-X data. Results reveal that the clutter suppression can be effectively achieved.
KW - Moving target detection
KW - sequential images
KW - synthetic aperture radar (SAR)
KW - urban areas
UR - https://www.scopus.com/pages/publications/85097955547
U2 - 10.1109/LGRS.2020.3034682
DO - 10.1109/LGRS.2020.3034682
M3 - 文章
AN - SCOPUS:85097955547
SN - 1545-598X
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -