补丁分布建模框架自适应余弦估计器（PaDiM-ACE）用于合成孔径雷达图像中的异常检测与定位
Patch distribution modeling framework adaptive cosine estimator (PaDiM-ACE) for anomaly detection and localization in synthetic aperture radar imagery
AI总结：
提出PaDiM-ACE方法，将ACE检测统计量引入PaDiM框架，用余弦相似度替代马氏距离，实现SAR图像有界异常检测与定位，提升AUROC性能。
摘要：
本文提出了一种用于合成孔径雷达（SAR）图像异常检测与定位的新方法，扩展了现有的补丁分布建模框架（PaDiM）。我们引入了自适应余弦估计器（ACE）检测统计量。PaDiM在推理时使用马氏距离，这是一种无界度量。而ACE使用余弦相似度度量，提供有界的异常检测分数。所提出的方法在多个SAR数据集上进行了评估，性能指标包括图像级和像素级的接收者操作特征曲线下面积（AUROC），旨在提高SAR图像异常检测与定位的性能。代码公开可用：https://github.com/Advanced-Vision-and-Learning-Lab/PaDiM-ACE。
英文摘要：
This work presents a new approach to anomaly detection and localization in synthetic aperture radar imagery (SAR), expanding upon the existing patch distribution modeling framework (PaDiM). We introduce the adaptive cosine estimator (ACE) detection statistic. PaDiM uses the Mahalanobis distance at inference, an unbounded metric. ACE instead uses the cosine similarity metric, providing bounded anomaly detection scores. The proposed method is evaluated across multiple SAR datasets, with performance metrics including the area under the receiver operating curve (AUROC) at the image and pixel level, aiming for increased performance in anomaly detection and localization of SAR imagery. The code is publicly available: https://github.com/Advanced-Vision-and-Learning-Lab/PaDiM-ACE.
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。
