Anomaly Detection of Hyperspectral Images Based on Transformer With Spatial–Spectral Dual-Window Mask

Abstract

Anomaly detection has become one of the crucial tasks in hyperspectral images processing. However, most deep learning-based anomaly detection methods often suffer from the incapability of utilizing spatial–spectral information, which decreases the detection accuracy. To address this problem, we propose a novel hyperspectral anomaly detection method with a spatial–spectral dual-window mask transformer, termed as S2DWMTrans, which can fully extract features from global and local perspectives, and suppress the reconstruction of anomaly targets adaptively. Specifically, the dual-window mask transformer aggregates background information of the entire image from a global perspective to neutralize anomalies, and uses neighboring pixels in a dual-window to suppress anomaly reconstruction. An adaptive-weighted loss function is designed to further suppress anomaly reconstruction adaptively during network training process. According to our investigation, this is the first work to apply transformer to hyperspectral anomaly detection. Comparative experiments and ablation studies demonstrate that the proposed S2DWMTrans achieves competitive performance.