A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Since the advent of hyperspectral remote sensing in the 1980s, it has made important
achievements in aerospace and aviation field and been applied in many fields …

Hyperspectral image (HSI) classification has attracted wide attention in many fields.
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …

Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …

Hyperspectral image classification (HSIC) is essential in remote sensing image analysis.
Applying a graph neural network (GNN) to hyperspectral image (HSI) classification has …

Feature matching, which refers to establishing reliable correspondence between two sets of
features (particularly point features), is a critical prerequisite in feature-based registration. In …

Deep learning methods have been successfully applied to learn feature representations for
high‐dimensional data, where the learned features are able to reveal the nonlinear …

Purpose Wireless capsule endoscopy (WCE) enables physicians to examine the digestive
tract without any surgical operations, at the cost of a large volume of images to be analyzed …

Limited by the shape-fixed kernels, convolutional neural networks (CNNs) are usually
difficult to model difform land covers in hyperspectral images (HSIs), leading to inadequate …

Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-
level data analysis, has been garnering growing attention in the remote sensing community …