Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement

TL;DR

This paper introduces a 3D spatial decomposition model that enhances moving target shadows in Video-SAR images by exploiting sparse, low-rank, and Gaussian properties, significantly improving detection and tracking accuracy.

Contribution

It proposes a novel shadow-background-noise decomposition method using sparse, low-rank, and Gaussian properties with ADMM, improving shadow detection and tracking in Video-SAR images.

Findings

Boosts shadow saliency in Video-SAR images.

Improves detection accuracy of Faster R-CNN, RetinaNet, YOLOv3.

Enhances tracking accuracy of TransTrack, FairMOT, ByteTrack.

Abstract

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.