In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, 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 …

Convolutional neural networks (CNNs) have been extensively applied to hyperspectral (HS)
image classification tasks and achieved promising performance. However, for CNN-based …

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

Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …

Hyperspectral image (HSI) classification is currently a hot topic in the field of remote sensing.
The goal is to utilize the spectral and spatial information from HSI to accurately identify land …

Hyperspectral image (HSI) classification is an important task in earth observation missions.
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI)
classification tasks because of their excellent local spatial feature extraction capabilities …

Convolutional neural networks (CNNs) have become one of the most popular tools to tackle
hyperspectral image (HSI) classification tasks. However, CNN suffers from the long-range …