Transformer-Based Autoencoder Framework for Nonlinear Hyperspectral Anomaly Detection
Ziyu Wu, Bin Wang
Abstract
Recently, the autoencoder (AE) has received significant attention in the hyperspectral anomaly detection task. However, all existing AE-based anomaly detectors operate under the linear mixing model, which cannot accurately model the nonlinear mixing phenomenon in practical hyperspectral images (HSIs). Moreover, these AE-based detectors rarely consider the spatial information between pixels, which is crucial to obtain accurate results of anomaly detection. To address the above issues, this paper proposes a transformer-based AE framework (TAEF) for nonlinear hyperspectral anomaly detection. Specifically, the proposed AE framework adopts the transformer as the encoder so that not only the local spatial information, but also the transitive global spatial information can be considered. And the extended multilinear mixing model (EMLM) is embedded into the decoder to accurately characterize the high-order nonlinear mixing phenomenon. By using this transformer-based AE framework, the background of HSIs can be reconstructed effectively. Moreover, a novel method for generating patches is proposed in this paper to support the transformer in the characterization of the transitive global spatial information. Besides, to further improve the accuracy of the background reconstruction, the local-clustering method is adopted to decrease the potential anomalies and increase the sparse backgrounds in the meantime. Finally, the anomalous level of pixel is calculated by the reconstruction error. The experimental results on various real hyperspectral datasets demonstrate that the proposed TAEF outperforms the current state-of-the-art anomaly detectors. In addition, our code is available at: https://github.com/I3ab/TAEF.