With the development of hyperspectral sensors, more and more hyperspectral images can
be acquired, and the pixel-oriented classification of hyperspectral images has attracted the …

Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI)
classification. However, the fixed receptive field of CNN-based methods limits their capability …

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

The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds
tremendous potential across a wide array of fields. By leveraging AI, the processing and …

Transformer is a powerful tool for capturing long-range dependencies and has shown
impressive performance in hyperspectral image (HSI) classification. However, such power …

In recent years, convolutional neural networks (CNNs) have achieved remarkable success
in hyperspectral image (HSI) classification tasks, primarily due to their outstanding spatial …

As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …

More recently, vision transformer (ViT) has shown competitive performance with
convolutional neural network (CNN) on computer vision tasks, which provided more …

Hyperspectral image (HSI) clustering is challenging to divide all pixels into different clusters
because of the absent labels, large spectral variability and complex spatial distribution …

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