Automatic Target Recognition

Automatic Target Recognition (ATR) aims to automatically identify objects of interest in imagery from various sensors, such as radar and electro-optical cameras. Current research heavily emphasizes improving ATR robustness by addressing challenges like limited labeled data through techniques like transductive transfer learning and contrastive learning, often employing deep learning architectures such as CycleGANs. Furthermore, research focuses on handling noisy data (e.g., speckle in SAR images) and extending ATR to open-set scenarios where unknown object classes may be encountered during operation, improving the reliability and applicability of these systems in real-world settings. The advancements in ATR have significant implications for various fields, including military surveillance, autonomous vehicles, and environmental monitoring.