Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Hyperspectral image (HSI) classification is an important task in earth observation missions.
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In
recent years, hypergraph learning has attracted increasing attention due to its flexibility and …

Graph can achieve good performance to extract the low-dimensional features of
hyperspectral image (HSI). However, the present graph-based methods just consider the …

The chest X-ray is considered a significant clinical utility for basic examination and
diagnosis. The human lung area can be affected by various infections, such as bacteria and …

Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a
proper land-cover label. Recently, convolutional neural networks (CNNs) have shown …

The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired
multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called …

Recently, due to the powerful capability at modeling the long-range relationships,
Transformer-based methods have been widely explored in many research areas, including …

In recent years, deep learning has been successfully applied to hyperspectral image
classification (HSI) problems, with several convolutional neural network (CNN) based …

Scene classification of high spatial resolution (HSR) images can provide data support for
many practical applications, such as land planning and utilization, and it has been a crucial …