Multi-Gaussian Prototype-Based Background Modeling in Latent Space for Hyperspectral Anomaly Detection

Variational autoencoder (VAE)-based hyperspectral anomaly detection (HAD) methods enable background samples to form statistically structured distributions by imposing probabilistic constraints in the latent space. However, most existing VAE-based approaches rely on a single latent distribution to model the background, which limits their ability to characterize complex background diversity. In addition, anomaly detection is typically based solely on reconstruction error, without explicitly considering deviations in the latent distribution, leading to insufficient discrimination between anomalies and background. Therefore, we propose a latent-space HAD method based on multi-Gaussian prototypes. Specifically, a Gaussian mixture model (GMM) is introduced into the latent space of the VAE to perform multiprototype background modeling, where each prototype represents a distinct background distribution pattern. This strategy mitigates the problem of inaccurate modeling caused by overly simplified background distribution assumptions. In addition, to avoid prototype collapse caused by imbalanced competition among Gaussian components, a prototype competition modulation mechanism is designed. By adaptively adjusting the response of the dominant prototype, this mechanism promotes balanced participation among different prototypes, thereby improving the stability of latent-space modeling. Finally, an anomaly score is constructed by jointly considering reconstruction error and latent-space deviation, enabling anomaly characterization from both reconstruction and statistical perspectives. This design effectively improves the separability between anomalous targets and normal background samples. Experiments conducted on six hyperspectral datasets demonstrate that the proposed method achieves superior detection performance compared with nine state-of-the-art approaches. The results demonstrate the effectiveness of multi-Gaussian prototype modeling for improving the robustness and accuracy of HAD.