Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have
generated good progress. Meanwhile, graph convolutional networks (GCNs) have also …

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

Recently, graph neural networks (GNNs) have been demonstrated to be a promising
framework in investigating non-Euclidean dependency in hyperspectral (HS) images. Since …

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

Convolutional neural networks are widely used in the field of hyperspectral image
classification because of their excellent nonlinear feature extraction ability. However, as the …

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …

Hyperspectral image (HSI) classification aims to identify categories of hyperspectral pixels.
Recently, many convolutional neural networks (CNNs) have been designed to explore the …

Hyperspectral image (HSI) classification methods based on the graph convolutional network
(GCN) have received more attention because they can handle irregular regions by graph …

Graph convolutional networks (GCNs) are a promising approach for addressing the
necessity for long-range information in hyperspectral image (HSI) classification …

Traditional building and water segmentation methods are vulnerable to noise interference,
and hence, they could not avoid missed and false detections in the detection process …