OT-AD: Optimal Transport-Guided Transformer for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is one of the research hotspots within the domain of remote sensing image interpretation. The key is how to effectively distinguish the anomaly from the background. Existing methods usually rely on the assumption that the background of hyperspectral images conforms to multivariate Gaussian distributions. However, for real data, this assumption does not necessarily fit. Building upon the study of background distributions in real hyperspectral data, this paper leverages optimal transport theory to model the background reconstruction task. The optimal transport of reconstruction and background is mathematically proven to be converted into that of reconstruction and the original image. To facilitate the reconstruction process, the Transformer architecture is employed, due to its inherent ability to compute similarity weights. Based on the above, the Optimal transport-guided Transformer Anomaly Detector (OT-AD) is designed by applying the Wasserstein distance constraints derived from optimal transport theory to the deep network. Experimental validation conducted on multiple public datasets confirms the effectiveness and reliability of the proposed method. The code for this work is publicly available at https://github.com/jiojio0505/OT-AD.