[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …
spatial analytics in Geography. Although much progress has been made in exploring the …
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 …
Decoupled-and-coupled networks: Self-supervised hyperspectral image super-resolution with subpixel fusion
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with
the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform …
the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform …
Interactformer: Interactive transformer and CNN for hyperspectral image super-resolution
Due to rich spectral information, hyperspectral images (HSIs) have been widely used in
various fields. However, limited by imaging systems, the low spatial resolution of HSIs has …
various fields. However, limited by imaging systems, the low spatial resolution of HSIs has …
Model inspired autoencoder for unsupervised hyperspectral image super-resolution
This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-
spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high …
spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high …
MAC-Net: Model-aided nonlocal neural network for hyperspectral image denoising
Hyperspectral image (HSI) denoising is an ill-posed inverse problem. The underlying
physical model is always important to tackle this problem, which is unfortunately ignored by …
physical model is always important to tackle this problem, which is unfortunately ignored by …
Hyperspectral image super-resolution via knowledge-driven deep unrolling and transformer embedded convolutional recurrent neural network
Hyperspectral (HS) imaging has been widely used in various real application problems.
However, due to the hardware limitations, the obtained HS images usually have low spatial …
However, due to the hardware limitations, the obtained HS images usually have low spatial …
Symmetrical feature propagation network for hyperspectral image super-resolution
Single hyperspectral image (HSI) super-resolution (SR) methods using a auxiliary high-
resolution (HR) RGB image have achieved great progress recently. However, most existing …
resolution (HR) RGB image have achieved great progress recently. However, most existing …