Most hyperspectral image (HSI) classification methods rely on square patch sampling to
incorporate spatial information, thereby facilitating the label prediction of the center pixel …

The success of vision Transformers (ViTs) relies heavily on the self-attention mechanism,
which requires support from appropriate patch tokenization. However, hyperspectral image …

Transformer has emerged as a preferred method for hyperspectral (HS) image classification
due to its ability to model long-range dependency. Whereas the transformer contains …

Currently, the convolutional neural networks (CNNs) have become the mainstream methods
for hyperspectral image (HSI) classification, due to their powerful ability to extract local …

Recently, there have been significant advancements in hyperspectral image (HSI)
classification methods employing Transformer architectures. However, these methods, while …

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

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

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

Hyperspectral image (HSI) classification has become a popular research topic in recent
years, and transformer-based networks have demonstrated superior performance by …

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