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 …

To improve the accuracy and generalization ability of hyperspectral image classification, a
feature extraction method integrating principal component analysis (PCA) and local binary …

Supervised classification of hyperspectral image (HSI) is generally required to obtain better
performance in spectral–spatial feature learning by fully using complex pixel-and superpixel …

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

Recently, convolutional neural networks (CNNs) have exhibited commendable performance
for hyperspectral image (HSI) classification. Generally, an important number of samples are …

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

Deep learning (DL) has become a mainstream method of hyperspectral image (HSI)
classification. Many DL-based methods exploit spatial-spectral features to achieve better …

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 …

The existing hyperspectral image (HSI) classification encounters the obstacle of improving
the classification accuracy with limited labeled samples. In this context, as a typical …