Dual-Frequency Autoencoder for Anomaly Detection in Transformed Hyperspectral Imagery

Abstract

Hyperspectral anomaly detection (HAD) is a challenging task since samples are unavailable for training. Although unsupervised learning methods have been developed, they often train the model using an original hyperspectral image (HSI) and require retraining on different HSIs, which may limit the feasibility of HAD methods in practical applications. To tackle this problem, we propose a dual-frequency autoencoder (DFAE) detection model in which the original HSI is transformed into high-frequency components (HFCs) and low-frequency components (LFCs) before detection. A novel spectral rectification is first proposed to alleviate the spectral variation problem and generate the LFCs of HSI. Meanwhile, the HFCs are extracted by the Laplacian operator. Subsequently, the proposed DFAE model is learned to detect anomalies from the LFCs and HFCs in parallel. Finally, the learned model is well-generalized for anomaly detection from other hyperspectral datasets. While breaking the dilemma of limited generalization in the sample-free HAD task, the proposed DFAE can enhance the background–anomaly separability, providing a better performance gain. Experiments on real datasets demonstrate that the DFAE method exhibits competitive performance compared with other advanced HAD methods.