Abstract: 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.
Publication date: April 2025
Link: https://arxiv.org/abs/2504.08115
Citation: L. Chauvin, S. Gupta, A. Ibarra and J. Peeples, “Benchmarking suite for synthetic aperture radar imagery anomaly detection (SARIAD) algorithms,” in Algorithms for Synthetic Aperture Radar Imagery XXXII, International Society for Optics and Photonics (SPIE), 2025, in Press, doi: arXiv:2504.08115.