Anomaly detection is crucial in various applications, particularly cybersecurity and network
intrusion. However, a common challenge across anomaly detection techniques is the …

Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.
HAD can find pixels with anomalous spectral signatures compared with their neighbor …

Considerable endeavors have been expended toward enhancing the representation
performance for hyperspectral anomaly detection (HAD) through physical model-based …

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 …

Accurately distinguishing between background and anomalous objects within hyperspectral
images poses a significant challenge. The primary obstacle lies in the inadequate modeling …

Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings, and is an active area of research in hyperspectral image processing …

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels
as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually …

Recently, the autoencoder (AE)-based methods in hyperspectral anomaly detection (HAD)
have attracted a lot of attention from scholars and researchers, and they acquire satisfying …