Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

arXiv:2404.13342v1 Announce Type: cross
Abstract: 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 …

abstract anomaly anomaly detection arxiv components cs.cv cs.lg detection however low norm prior representation spatial type