Self-Supervised Contrastive Learning for Hyperspectral Anomaly Detection With Block-Pseudolabel Masked Data Augmentation

Hyperspectral anomaly detection (HAD) aims to identify targets deviating from normal patterns of background. However, the lack of labeled samples pose significant challenges to the task. Self-supervised deep learning methods have shown promising results in this scenario, but they often fall into partial anomaly representations, causing high false alarm rates. Effectively leveraging task knowledge has become a key point. In this paper, we propose a hyperspectral anomaly detection method based on blind-block masked data augmentation and self-supervised contrastive learning. First, the hyperspectral data is divided into patches, followed by patch classification and data augmentation to generate accurate prior knowledge for the learning model. Second, patch pairs with their pseudo-label and corresponding masks are used to train a lightweight residual network integrating contrastive learning, in order to improve detection accuracy while avoiding high computational cost of pixel- wise reconstruction. Finally, pseudo-label is introduced into the detection process again to further guide the anomaly localization. Experimental results show that the proposed method achieves outstanding performance on various hyperspectral datasets, demonstrating strong background-anomaly separability and superior detection accuracy.