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 …

Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …

Most domain adaptation (DA) methods in cross-scene hyperspectral image classification
focus on cases where source data (SD) and target data (TD) with the same classes are …

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

By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution
multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a …

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 …

Hyperspectral image super-resolution (HISR) is about fusing a low-resolution hyperspectral
image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high …

Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …

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

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 …