Hyperspectral (HS) pansharpening consists of fusing a high-resolution panchromatic (PAN)
band and a low-resolution HS image to obtain a new image with high resolution in both the …

Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN)
image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution …

Image fusion can be conducted at different levels, with pixel-level image fusion involving the
direct combination of original information from source images. The objective of methods …

Multi-task learning has commonly been used and performed well at joint visual perception
tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract …

In recent years, deep learning techniques for pansharpening multiresolution images have
gained increasing interest. Due to the lack of ground truth data, most deep learning solutions …

Standard hyperspectral (HS) pansharpening utilizes panchromatic (PAN) images to improve
the connected low (spatial) resolution HS (LRHS) images to the spatial resolutions of PANs …

Hyperspectral images (HSIs) are frequently noisy and of low resolution due to the
constraints of imaging devices. Recently launched satellites can concurrently acquire HSIs …

Sentinel-5P is a valuable resource for academics and policymakers. The ability of the
satellite's equipment to span the electromagnetic spectrum from ultraviolet (UV) to short …

Target monitoring is an important subject in machine vision. Hyperspectral image (HSI) can
effectively assist the target detection and recognition effect of traditional optical images …

From the perspective of optimization, most of the current mainstream remote sensing data
fusion methods are based on traditional mathematical optimization or single objective …