The booming development of remote sensing images in many visual tasks has led to an
increasing demand for obtaining images with more precise details. However, it is impractical …

Hyperspectral image super-resolution, which refers to reconstructing the high-resolution
hyperspectral image from the input low-resolution observation, aims to improve the spatial …

Convolutional neural networks (CNNs) have recently achieved impressive improvements on
hyperspectral (HS) pansharpening. However, most of the CNN-based HS pansharpening …

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN)
images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this …

Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …

Traditional approaches mainly fuse a hyperspectral image (HSI) with a high-resolution
multispectral image (MSI) to improve the spatial resolution of the HSI. However, such …

Target detection plays a core issue in hyperspectral remote sensing, but faces serious
challenges of how to deal with the spatial and spectral redundancies and spectral variations …

Hyperspectral (HS) pansharpening seeks to integrate low spatial resolution HS (LRHS)
images with connected panchromatic (PAN) images to produce high spatial resolution HS …

Despite recent advances achieved by deep learning techniques in the fusion of low-spatial-
resolution hyperspectral image (LR-HSI) and high-spatial-resolution multispectral image …

Fusion of hyperspectral images (HSIs) with low-spatial and high-spectral resolution and
multispectral images (MSIs) with high-spatial and low-spectral resolution is an important …