Recently, autoencoder (AE)-based hyperspectral anomaly detection methods have
demonstrated excellent performance on hyperspectral images (HSIs). The AE can …

A known target detection assumes that the target to be detected is provided a priori, while
anomaly detection is an unknown target detection without any prior knowledge. As a result …

Unsupervised anomaly detection and localization in images is a challenging problem,
leading previous methods to attempt an easier supervised one-class classification …

Hyperspectral target detection (HTD) and hyperspectral anomaly detection (HAD) are
designed by completely different functionalities in terms of how to carry out target detection …

Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted …

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial
issues, anomaly detectability and background suppressibility (BS), both of which are very …

Anomaly detection (AD) requires spectral and spatial information to differentiate anomalies
from their surrounding data samples. To capture spatial information, a general approach is …

Surveillance systems are playing a significant role in law enforcement and city safety. It is
important to detect violent and suspicious behaviors automatically in video surveillance …

Anomaly detection aims to identify unusual behavior or discriminate abnormal samples by
referring to the normal samples of data. Most exiting anomaly detection approaches train the …

Low-rank and sparse representation (LRaSR)-based approaches have been widely used for
anomaly detection (AD). Their central ideas are to minimize the rank of the low-rank space …