Unsupervised Abundance Matrix Reconstruction Transformer-Guided Fractional Attention Mechanism for Hyperspectral Anomaly Detection.

Hyperspectral anomaly detection (HAD), a challenging inverse problem, has found numerous scientific applications. Although extant HAD algorithms have achieved remarkable results, there are still several issues remained unresolved: 1) low spatial resolution (and spectral redundancy) in typical hyperspectral images prevents effectively distinguishing the abnormal pixels from those normal ones and 2) the reconstruction from existing residual-based frameworks would not completely remove anomaly effects, making the detection solely from the residual impractical. In this article, we propose a novel HAD method, termed transformer-guided fractional attention within the abundance domain (TGFA-AD), which substitutes raw input image with the abundance matrix obtained via blind source separation (BSS). First, the proposed abundance spatial-channel reconstruction transformer (ASCR-Former) is customized for rebuilding the abundance matrix. According to the image self-similarity, the abundance is patch-wisely encoded with class (CLS) tokens. The transformer encoders intensify the spatial and channel characteristics between tokens for reconstructing the abundance, followed by deriving the initial detection from the abundance residual matrix. Second, a novel fractional abundance attention (FAA) mechanism is proposed, where the attention weights coming from a specific linear combination of abundances are guided by the initial detection with convex [Formula: see text] -quadratic norm. Finally, the fractional convolution is incorporated to fuse the abundance and residual into the fractional feature for yielding the final detection result. Real data experiments quantitatively and qualitatively exhibit the state-of-the-art performance of TGFA-AD.