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

For hyperspectral image (HSI) classification, two branch networks generally use
convolutional neural networks (CNNs) to extract the spatial features and long short-term …

Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC)
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …

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

Masked image modeling (MIM) is a highly popular and effective self-supervised learning
method for image understanding. The existing MIM-based methods mostly focus on spatial …

Despite the success of deep learning-based change detection (CD) methods, their existing
insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them …

Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral
image classification for its excellent feature representation and convolution on arbitrarily …

Transformer has been widely used in classification tasks for hyperspectral images (HSIs) in
recent years. Because it can mine spectral sequence information to establish long-range …

Dimensionality reduction (DR) is important for feature extraction and classification of
hyperspectral images (HSIs). Recently proposed superpixel-based DR models have shown …

The effective combination of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data can be used for land cover classification. Recently, deep-learning-based …