Zero-Shot Hyperspectral Anomaly Detection via Structure-Inconsistency Modeling and Guided Enhancement

Hyperspectral anomaly detection (HAD) aims to identify anomalous targets deviating from the background in unlabeled hyperspectral images. Self-supervised methods typically model the background using selected training samples and detect anomalies from reconstruction residuals, but they often weaken target signals, resulting in low detection accuracy. Supervised approaches using annotated or simulated targets highlight anomaly centers well but struggle to preserve edge details. To address these li