首个面向合成孔径雷达图像异常检测的基准套件，支持算法评测与复现。
Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms
- 整合多源SAR数据集与深度学习工具，统一异常检测流程。
- 提供多种评估指标与可视化功能，支持模型性能对比。
- 适合从事遥感图像分析、雷达异常检测的研究者使用。
异常检测是计算机视觉与机器学习中的关键挑战，广泛应用于质量控制、雷达成像等领域。在合成孔径雷达（SAR）成像中，异常检测可用于目标分类、检测与分割。然而，目前缺乏针对SAR图像异常检测方法的开发与评测框架。为此，本文提出SAR影像异常检测基准套件（SARIAD），结合Anomalib深度学习库，构建了涵盖多种SAR数据集与算法评估工具的完整体系。SARIAD支持多种异常检测算法在SAR图像上的应用，并提供多种评估指标与可视化手段。整体上，SARIAD作为统一包，推动了SAR图像异常检测领域的可复现研究。该工具包已开源：https://github.com/Advanced-Vision-and-Learning-Lab/SARIAD。
原文摘要 · Abstract (English)
Anomaly detection is a key research challenge in computer vision and machine learning with applications in many fields from quality control to radar imaging. In radar imaging, specifically synthetic aperture radar (SAR), anomaly detection can be used for the classification, detection, and segmentation of objects of interest. However, there is no method for developing and benchmarking these methods on SAR imagery. To address this issue, we introduce SAR imagery anomaly detection (SARIAD). In conjunction with Anomalib, a deep-learning library for anomaly detection, SARIAD provides a comprehensive suite of algorithms and datasets for assessing and developing anomaly detection approaches on SAR imagery. SARIAD specifically integrates multiple SAR datasets along with tools to effectively apply various anomaly detection algorithms to SAR imagery. Several anomaly detection metrics and visualizations are available. Overall, SARIAD acts as a central package for benchmarking SAR models and datasets to allow for reproducible research in the field of anomaly detection in SAR imagery. This package is publicly available: https://github.com/Advanced-Vision-and-Learning-Lab/SARIAD.
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