Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures
are different from their background. Anomalies are often man-made targets. With such target …

In this article, we introduce a new approach to address the challenge of generalization in
hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting …

In this paper, we propose a novel sparsity-based algorithm for anomaly detection in
hyperspectral imagery. The algorithm is based on the concept that a background pixel can …

The massive use of information and communication technologies in supervisory control and
data acquisition (SCADA) systems opens new ways for carrying out cyberattacks against …

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

Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology
in remote sensing and Earth science domains. Nevertheless, most existing detection …

Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-
background modeling. Deep learning has been applied to HAD and achieves promising …

Hyperspectral anomaly detection aims at distinguishing targets of interest from the
background without prior knowledge. Although low-rank representation (LRR)-based …