Hyperspectral image super-resolution meets deep learning: A survey and perspective
Hyperspectral image super-resolution, which refers to reconstructing the high-resolution
hyperspectral image from the input low-resolution observation, aims to improve the spatial …
hyperspectral image from the input low-resolution observation, aims to improve the spatial …
A review of spatial enhancement of hyperspectral remote sensing imaging techniques
N Aburaed, MQ Alkhatib, S Marshall… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
Zero-shot hyperspectral sharpening
Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
PSRT: Pyramid shuffle-and-reshuffle transformer for multispectral and hyperspectral image fusion
A Transformer has received a lot of attention in computer vision. Because of global self-
attention, the computational complexity of Transformer is quadratic with the number of …
attention, the computational complexity of Transformer is quadratic with the number of …
GuidedNet: A general CNN fusion framework via high-resolution guidance for hyperspectral image super-resolution
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 …
image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high …
Diffusion model with disentangled modulations for sharpening multispectral and hyperspectral images
The denoising diffusion model has received increasing attention in the field of image
generation in recent years, thanks to its powerful generation capability. However, diffusion …
generation in recent years, thanks to its powerful generation capability. However, diffusion …
Zero-shot semi-supervised learning for pansharpening
Pansharpening refers to fusing a low-resolution multispectral image (LRMS) and a high-
resolution panchromatic (PAN) image to generate a high-resolution multispectral image …
resolution panchromatic (PAN) image to generate a high-resolution multispectral image …
A novel spatial fidelity with learnable nonlinear mapping for panchromatic sharpening
The purpose of panchromatic (PAN) sharpening, ie, pansharpening, is to fuse a low spatial
resolution multispectral (LRMS) image with a high spatial resolution PAN image, aiming to …
resolution multispectral (LRMS) image with a high spatial resolution PAN image, aiming to …
Fusionmamba: Efficient image fusion with state space model
Image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a
high-resolution image with limited spectral information and a low-resolution image with …
high-resolution image with limited spectral information and a low-resolution image with …
Unsupervised hyperspectral pansharpening via low-rank diffusion model
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 and a low-resolution hyperspectral (LRHS) image to create a single high-resolution …