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 …

Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

Raman spectroscopy is a powerful technique widely used in analytical chemistry. However,
spectral noise emerging during detection introduces potential to compromise the signal-to …

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the
performance of the subsequent HSI interpretation and applications. In this paper, a novel …

Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …

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 …

Feature extraction is of significance for hyperspectral image (HSI) classification. Compared
with conventional hand-crafted feature extraction, deep learning can automatically learn …

In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal
method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In …

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

The spectral and the spatial information in hyperspectral images (HSIs) are the two sides of
the same coin. How to jointly model them is the key issue for HSIs' noise removal, including …