  Moving target shadows among video synthetic aperture radar (Video-SAR) images
are always interfered by low scattering backgrounds and cluttered noises,
causing poor detec-tion-tracking accuracy. Thus, a shadow-background-noise 3D
spatial decomposition (SBN-3D-SD) model is proposed to enhance shadows for
higher detection-tracking accuracy. It leverages the sparse property of
shadows, the low-rank property of back-grounds, and the Gaussian property of
noises to perform 3D spatial three-decomposition. It separates shadows from
back-grounds and noises by the alternating direction method of multi-pliers
(ADMM). Results on the Sandia National Laboratories (SNL) data verify its
effectiveness. It boosts the shadow saliency from the qualitative and
quantitative evaluation. It boosts the shadow detection accuracy of Faster
R-CNN, RetinaNet and YOLOv3. It also boosts the shadow tracking accuracy of
TransTrack, FairMOT and ByteTrack.
