Granularity-Inconsistent Transformer for Unsupervised Hyperspectral Anomaly Detection

In hyperspectral anomaly detection (HAD), anomalous pixels typically exhibit a sparsely distributed spatial pattern. Existing deep models often generate backgrounds by reconstructing spectral vectors, yet fail to capture the inherent spatial characteristics of the image. To overcome the semantic and structural information loss caused by neglecting spatial features, we propose the granularity-inconsistent transformer (GIFormer) for unsupervised HAD. Specifically, the interaction between the spatial and spectral dimensions is leveraged to enhance the spatial-spectral feature representation of hyperspectral images, highlighting the differences between background and anomaly features. The GIFormer performs multilevel background reconstruction to detect anomalies. In the encoder, patch-level anomaly elimination masks are applied to reconstruct background features, where spatial correlations of anomalies are utilized to suppress anomalous patterns spanning multiple pixels. The decoder operates at the pixel level, using fine-grained receptive fields for global attention modeling, which enables the model to refine local details that may have been aggregated by the encoder in larger patches, ensuring the final reconstruction retains the intricate structure of the original hyperspectral data. Furthermore, adaptive weight loss is incorporated to guide network training. Extensive experimental results confirm the superior performance of GIFormer.