SE-BSFV：复杂背景下ViSAR中基于在线子空间学习的阴影增强与背景抑制
SE-BSFV: Online Subspace Learning based Shadow Enhancement and Background Suppression for ViSAR under Complex Background
AI总结：
针对ViSAR运动目标检测中阴影难与背景低散射区区分的问题，提出基于LRR理论和在线子空间学习的SE-BSFV算法，经配准、建模、参数估计和强散射消除等步骤，有效增强阴影显著性并提升检测性能。
摘要：
视频合成孔径雷达（ViSAR）因能够持续监测目标区域的变化，在运动目标检测（MTD）领域受到了广泛关注。在ViSAR中，运动目标的阴影不会发生偏移和散焦，这一特性被广泛用作MTD的特征。然而，阴影难以与背景中的低散射区域区分开，这会导致更多的漏检和虚警。因此，研究如何增强阴影与背景之间的区分度具有重要价值。本研究提出了SE-BSFV（Shadow Enhancement and Background Suppression for ViSAR，ViSAR阴影增强与背景抑制）算法。SE-BSFV算法基于低秩表示（LRR）理论，采用在线子空间学习技术来增强ViSAR图像中的阴影并抑制背景。首先，我们使用配准算法对ViSAR图像进行配准，并利用高斯混合分布（GMD）对ViSAR数据建模。其次，利用从先前帧中学到的知识来估计当前帧的GMD参数，并使用期望最大化（EM）算法估计子空间参数，随后即可得到当前帧的前景矩阵。最后，使用交替方向乘子法（ADMM）消除前景矩阵中的强散射目标，以得到最终结果。实验结果表明，与其他几种先进的预处理算法相比，SE-BSFV算法在保证效率的同时，显著增强了阴影的显著性，极大提升了检测性能。
英文摘要：
Video synthetic aperture radar (ViSAR) has attracted substantial attention in the moving target detection (MTD) field due to its ability to continuously monitor changes in the target area. In ViSAR, the moving targets' shadows will not offset and defocus, which is widely used as a feature for MTD. However, the shadows are difficult to distinguish from the low scattering region in the background, which will cause more missing and false alarms. Therefore, it is worth investigating how to enhance the distinction between the shadows and background. In this study, we proposed the Shadow Enhancement and Background Suppression for ViSAR (SE-BSFV) algorithm. The SE-BSFV algorithm is based on the low-rank representation (LRR) theory and adopts online subspace learning technique to enhance shadows and suppress background for ViSAR images. Firstly, we use a registration algorithm to register the ViSAR images and utilize Gaussian mixture distribution (GMD) to model the ViSAR data. Secondly, the knowledge learned from the previous frames is leveraged to estimate the GMD parameters of the current frame, and the Expectation-maximization (EM) algorithm is used to estimate the subspace parameters. Then, the foreground matrix of the current frame can be obtained. Finally, the alternating direction method of multipliers (ADMM) is used to eliminate strong scattering objects in the foreground matrix to obtain the final results. The experimental results indicate that the SE-BSFV algorithm significantly enhances the shadows' saliency and greatly improves the detection performance while ensuring efficiency compared with several other advanced pre-processing algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
