  The majority of existing hyperspectral anomaly detection (HAD) methods use
the low-rank representation (LRR) model to separate the background and anomaly
components, where the anomaly component is optimized by handcrafted sparse
priors (e.g., $\ell_{2,1}$-norm). However, this may not be ideal since they
overlook the spatial structure present in anomalies and make the detection
result largely dependent on manually set sparsity. To tackle these problems, we
redefine the optimization criterion for the anomaly component in the LRR model
with a self-supervised network called self-supervised anomaly prior (SAP). This
prior is obtained by the pretext task of self-supervised learning, which is
customized to learn the characteristics of hyperspectral anomalies.
Specifically, this pretext task is a classification task to distinguish the
original hyperspectral image (HSI) and the pseudo-anomaly HSI, where the
pseudo-anomaly is generated from the original HSI and designed as a prism with
arbitrary polygon bases and arbitrary spectral bands. In addition, a
dual-purified strategy is proposed to provide a more refined background
representation with an enriched background dictionary, facilitating the
separation of anomalies from complex backgrounds. Extensive experiments on
various hyperspectral datasets demonstrate that the proposed SAP offers a more
accurate and interpretable solution than other advanced HAD methods.
