Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

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

Recent years have witnessed the flourishing of deep learning-based methods in
hyperspectral anomaly detection (HAD). However, the lack of available supervision …

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 …

Recently, low-rank representation (LRR) methods have been widely applied for
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …

Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that deviate from
the surrounding background in unlabeled hyperspectral images (HSIs). Most existing deep …

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

Recent years have witnessed significant advances of deep learning technology in
hyperspectral anomaly detection (HAD). Among these methods, existing unsupervised …