Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

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

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …

Model-driven methods and data-driven methods have been widely developed for
hyperspectral image (HSI) denoising. However, there are pros and cons in both model …

Although deep neural networks (DNNs) have been widely applied to hyperspectral image
(HSI) denoising, most DNN-based HSI denoising methods are designed by stacking …

Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types,
which degrades the quality of the acquired image and limits the subsequent application. In …

Hyperspectral image (HSI) denoising is an ill-posed inverse problem. The underlying
physical model is always important to tackle this problem, which is unfortunately ignored by …

Hyperspectral image (HSI) mixed noise removal is a fundamental problem and an important
preprocessing step in remote sensing fields. The low-rank approximation-based methods …