Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …

Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a
crucial factor in anomaly detection (AD) evaluation. The difficulty in dealing with BS arises …

Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder
(DAE) models have been proven to be effective for the task of anomaly detection (AD) in …

Sparse representation (SR)-based approaches and collaborative representation (CR)-
based methods are proved to be effective to detect the anomalies in a hyperspectral image …

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 …

The low rank and sparse representation (LRSR) technique has attracted increasing attention
for hyperspectral anomaly detection (HAD). Although a large quantity of research based on …

Hyperspectral anomaly detection based on representation learning has received much
attention in recent years. Due to the lack of prior knowledge about anomalies, it is difficult for …

Autoencoders (AEs) are central to hyperspectral anomaly detection, given their impressive
efficacy. However, the current methodologies often neglect the global pixel similarity of the …

This article addresses the problem of the building an out-of-the-box deep detector, motivated
by the need to perform anomaly detection across multiple hyperspectral images (HSIs) …