The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential
information of land objects through contiguous narrow spectral wavelength bands. The …

Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

Recently, embedding and metric-based few-shot learning (FSL) has been introduced into
hyperspectral image classification (HSIC) and achieved impressive progress. To further …

Deep neural networks have been widely applied to hyperspectral image (HSI) classification
areas, in which recurrent neural network (RNN) is one of the most typical networks. Most of …

Supervised machine learning and deep learning methods perform well in hyperspectral
image classification. However, hyperspectral images have few labeled samples, which …

Hyperspectral images (HSIs) contain abundant information in the spatial and spectral
domains, allowing for a precise characterization of categories of materials. Convolutional …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …

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

Recently, deep learning models have achieved excellent performance in hyperspectral
image (HSI) classification. Among the many deep models, Transformer has gradually …