Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots
in the field of remote sensing. Because HSI's features of integrating image and spectrum …

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

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

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

Fusing hyperspectral images (HSIs) and multispectral images (MSIs) is an economic and
feasible way to obtain images with both high spectral resolution and spatial resolution. Due …

Without any prior information, hyperspectral anomaly detection is devoted to locating targets
of interest within a specific scene by exploiting differences in spectral characteristics …

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

The decrease in the widths of spectral bands in hyperspectral imaging leads to a decrease
in signal-to-noise ratio (SNR) of measurements. The decreased SNR reduces the reliability …

Recently, deep-learning-based denoising methods for hyperspectral images (HSIs) have
been comprehensively studied and achieved impressive performance because they can …

The plug-and-play (PnP) technique enables us to plug image priors into an alternating
direction method of multipliers (ADMM) framework for solving a regularized optimization …