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 …

Hyperspectral anomaly detection (HAD), which is widely used in military and civilian fields,
aims to detect the pixels with large spectral deviation from the background. Recently …

Hyperspectral anomaly detection (HAD) aims to label each hyperspectral image (HSI) pixel
as background or anomaly, in a totally unsupervised manner. Thus, a fine background …

As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection
has broad applications in many fields. Convex analysis (CA) is one of the fundamental …

Anomaly detection (AD) aiming to locate targets distinct from the surrounding background
spectra remains a challenging task in hyperspectral applications. The methods based on …

Hyperspectral anomaly detection has become an important branch of remote–sensing
image processing due to its important theoretical value and wide practical application …

Lately, generative adversarial networks (GAN)-based methods have drawn extensive
attention and achieved a promising performance in the field of hyperspectral anomaly …

Various anomaly detection (AD) methods focus on the background feature extraction and
suppression from hyperspectral images (HSIs). However, this process is susceptible to …

To achieve real-time abundance estimation of hyperspectral images and improve the
accuracy and efficiency of estimation, this article proposes a new band processing approach …