<ul><li>Anomaly detection is a key research challenge in computer vision and machine learning with applications in many fields from quality control to radar imaging.</li><li>In radar imaging, specifically synthetic aperture radar (SAR), anomaly detection can be used for the classification, detection, and segmentation of objects of interest.</li><li>To address the lack of method for developing and benchmarking SAR imagery anomaly detection methods, the Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) suite is introduced.</li><li>SARIAD integrates multiple SAR datasets, various anomaly detection algorithms, and provides metric evaluation and visualization tools for benchmarking SAR models and datasets.</li></ul>

Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms

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