Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …

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 …

In recent years, the applications of graph convolutional networks (GCNs) in hyperspectral
image (HSI) classification have attracted much attention. However, hyperspectral …

Transformer has been extensively explored for hyperspectral image (HSI) classification.
However, transformer poses challenges in terms of speed and memory usage because of its …

Currently, the different deep neural network (DNN) learning approaches have done much for
the classification of hyperspectral images (HSIs), especially most of them use the …

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

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in
significant yield losses globally. Effective monitoring and accurate detection of yellow rust …

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …