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 methods use the low-rank representation (LRR) model to separate the background and anomaly components%2C where the anomaly component is optimized by handcrafted sparse priors (e.g.%2C %24ell_{2%2C1}%24-norm). However%2C 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%2C 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%2C which is customized to learn the characteristics of hyperspectral anomalies. Specifically%2C this pretext task is a classification task to distinguish the original hyperspectral image (HSI) and the pseudo-anomaly HSI%2C 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%2C a dual-purified strategy is proposed to provide a more refined background representation with an enriched background dictionary%2C 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.?quality=80&w=800)
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