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 …

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 …

The monitoring of coastal wetlands is of great importance to the protection of marine and
terrestrial ecosystems. However, due to the complex environment, severe vegetation …

Due to rich spectral and spatial information, the combination of hyperspectral and
multispectral images (MSIs) has been widely used for Earth observation, such as wetland …

As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …

Many deep learning models, such as convolutional neural network (CNN) and recurrent
neural network (RNN), have been successfully applied to extracting deep features for …

Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …

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 …

Label information plays an important role in a supervised hyperspectral image classification
problem. However, current classification methods all ignore an important and inevitable …

Several bandwise total variation (TV) regularized low-rank (LR)-based models have been
proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert …