Spectral Mixing Augmentation for Preventing False Positives from Hyperspectral Anomaly Detection

In hyperspectral imaging, anomaly detection models often struggle with spectral mixing due to low spatial resolution or complex surface interactions. Existing models fail to generalize well across diverse spectral mixing scenarios, particularly near objects with irregular shapes or sizes. This leads to high false positive rates and increased inspection overhead, especially in industrial applications such as food quality assessment. To address this challenge, we propose a novel data augmentation framework that leverages a linear spectral mixing model to capture diverse real-world spectral compositions. The augmented data is integrated into the training pipeline of anomaly detection models to enhance their robustness. Extensive evaluations on real-world hyperspectral datasets demonstrate that our approach significantly reduces false positives while improving model reliability. The proposed framework provides a scalable and effective solution for industrial anomaly detection and quality control tasks.