用改进的余弦相似度提升雷达图像异常检测精度
Patch distribution modeling framework adaptive cosine estimator (PaDiM-ACE) for anomaly detection and localization in synthetic aperture radar imagery
- 采用余弦相似度替代传统距离度量，输出有界异常分数
- 在多个雷达数据集上实现图像与像素级高AUROC值
- 适合需要精准定位异常的遥感图像分析场景
本文提出一种新型合成孔径雷达影像（SAR）异常检测与定位方法，基于现有的块分布建模框架（PaDiM），引入自适应余弦估计器（ACE）作为检测统计量。与原方法在推理时使用无界的马氏距离不同，ACE采用余弦相似度，获得有界异常检测得分。该方法在多个SAR数据集上进行了评估，性能指标包括图像级和像素级的受试者工作特征曲线下面积（AUROC），旨在提升SAR影像中异常检测与定位的效果。代码已公开：https://github.com/Advanced-Vision-and-Learning-Lab/PaDiM-ACE。
原文摘要 · Abstract (English)
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.
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