论文标题
阴影 - 背景噪声3D空间分解使用稀疏的低级高斯属性，用于视频 - 萨尔移动目标阴影增强
Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement
论文作者
论文摘要
视频综合孔径雷达（视频 - 萨尔）图像之间的移动目标阴影总是会被低散射背景和混乱的噪音干扰，从而导致较差的探测跟踪准确性。因此，提出了一个暗影 - 背景噪声3D空间分解（SBN-3D-SD）模型，以增强阴影以提高检测跟踪准确性。它利用了阴影的稀疏特性，后台的低级别特性以及噪音的高斯属性来执行3D空间三分解。它通过多层的交替方向方法（ADMM）将阴影与后台和噪音分开。 Sandia国家实验室（SNL）数据的结果验证了其有效性。它从定性和定量评估中提高了阴影显着性。它提高了更快的R-CNN，视网膜和Yolov3的阴影检测精度。它还提高了TransTrack，Fairmot和Bytetrack的阴影跟踪精度。
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.
