The rich and distinguishable spectral information in hyperspectral images (HSIs) makes it
possible to capture anomalous samples [ie, anomaly detection (AD)] that deviate from …

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 …

Limited by the anomalous spectral vectors in unlabeled hyperspectral images (HSIs),
anomaly detection methods based on background distribution estimation often suffer from …

Anomaly detection (AD) using hyperspectral images (HSIs) is of great interest for deep
space exploration and Earth observations. This article proposes a weakly supervised …

Theoretically, hyperspectral images (HSIs) are capable of providing subtle spectral
differences between different materials, but in fact, it is difficult to distinguish between …

Hyperspectral anomaly detection (HAD) is a research endeavor of high practical relevance
within remote sensing scene interpretation. In this work, we propose an unsupervised …

Hyperspectral images (HSIs) have unique advantages in distinguishing subtle spectral
differences of different materials. However, due to complex and diverse backgrounds …

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

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