With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial
resolution owing to the limitations of imaging sensors. Image fusion is an effective and …

Considerable endeavors have been expended toward enhancing the representation
performance for hyperspectral anomaly detection (HAD) through physical model-based …

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 …

Deep learning (DL) methods have achieved state-of-the-art performance in the task of single
image rain removal. Most of current DL architectures, however, are still lack of sufficient …

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

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 …

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 …

The trade-off between spatial and spectral resolution is one of the fundamental issues in
hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution …

Pan-sharpening is an important technique for remote sensing imaging systems to obtain
high resolution multispectral images. Recently, deep learning has become the most popular …