Big databases are increasingly widespread and are therefore hard to understand, in
exploratory biomedicine science, big data in health research is highly exciting because data …

In the rapidly evolving realm of remote sensing technology, the classification of
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …

Salt-induced weathering is a common phenomenon in stone relics, and its traditional
artificial evaluation of severity is greatly affected by subjective consciousness and lacks …

In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture
for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current …

The current superresolution (SR) methods based on deep learning have shown remarkable
comparative advantages but remain unsatisfactory in recovering the high-frequency edge …

Hyperspectral image (HSI) spectral-spatial joint feature (FE) extraction methods generally
suffer from low feature retention and weak spatial–spectral dependence, which will lead to …

Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from
more and more researchers. HSI has abundant spectral and spatial information; thus, how to …

Many deep learning models, such as convolutional neural network (CNN) and recurrent
neural network (RNN), have been successfully applied to extracting deep features for …

Hyperspectral images (HSIs) contain abundant information in the spatial and spectral
domains, allowing for a precise characterization of categories of materials. Convolutional …