Hyperspectral image (HSI) classification is a critical task with numerous applications in the
field of remote sensing. Although convolutional neural networks have achieved remarkable …

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) …

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

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

In hyperspectral image (HSI) classification, objects corresponding to pixels of different
classes exhibit varying size characteristics, which causes a challenge for effective pixelwise …

Hyperspectral image (HSI) can provide rich spectral information which can be helpful for
accurate classification in many applications. Yet, incorporating spatial information in the …

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

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

Hyperspectral image (HSI) classification aims to identify categories of hyperspectral pixels.
Recently, many convolutional neural networks (CNNs) have been designed to explore the …