Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …

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 …

Recently, unmixing-based networks have shown significant potential in unsupervised
multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task …

The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the
spotlight. The combination of high spatial resolution MS images with HS data showing a …

Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a
low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-
resolution hyperspectral image from a low-resolution observation. However, the prevailing …

Hyperspectral images (HSIs) are of crucial importance in order to better understand features
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

In recent years, enormous efforts have been made to design image-processing algorithms to
enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly …