This paper reviews well-known classic algorithms and more recent experimental
approaches for distinguishing the weak signal of a target (either known or anomalous) from …

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian
background with an unknown covariance matrix. The anomaly to be detected occupies …

Hyperspectral sensors are passive instruments that record reflected electromagnetic
radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two …

Over the last two decades, anomaly detection (AD) has been known to play a critical role in
hyperspectral image analysis, which provides a new way to distinguish the targets from the …

Anomaly detection has been an important topic in hyperspectral image analysis. This
technique is sometimes more preferable than the supervised target detection because it …

In hyperspectral anomaly detection, anomalies are rare targets that exhibit distinct spectral
signatures from the background. Thus, anomalies are with low probabilities of occurrence in …

Anomaly detection (AD) in remotely sensed hyperspectral images has been proven to be
valuable in many applications. In this paper, we propose a scheme for detecting global …

A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first
two dimensions indicating the spatial domain and the third dimension indicating the spectral …

The generalized likelihood ratio test (GLRT) is here combined with the nonparametric
approach to derive a new adaptive detector for subpixel targets in hyperspectral images …

Anomaly detection is one of the practical applications in hyperspectral imagery (HSI) over
the last two decades. In this paper, we propose a combined similarity criterion anomaly …