Convolutional neural network (CNN) for hyperspectral image classification can provide
excellent performance when the number of labeled samples for training is sufficiently large …

Deep learning-based methods, especially deep convolutional neural network (CNN), have
proven their powerfulness in hyperspectral image (HSI) classification. On the other hand …

This paper studies the classification problem of hyperspectral image (HSI). Inspired by the
great success of deep neural networks in Artificial Intelligence (AI), researchers have …

Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has
also been successfully applied for hyperspectral image classification tasks. Training deep …

Hyperspectral image classification has become a research focus in recent literature.
However, well-designed features are still open issues that impact on the performance of …

As a powerful visual model, convolutional neural networks (CNNs) have demonstrated
remarkable performance in various visual recognition problems, and attracted considerable …

Convolutional neural networks (CNNs) have been extended to hyperspectral imagery (HSI)
classification due to its better feature representation and high performance, whereas …

Jointly using spatial and spectral information has been widely applied to hyperspectral
image (HSI) classification. Especially, convolutional neural networks (CNN) have gained …

Transformer-based networks, which can well model the global characteristics of inputted
data using the attention mechanism, have been widely applied to hyperspectral image (HSI) …

Hyperspectral image (HSI) classification is a core task in the remote sensing community, and
recently, deep learning-based methods have shown their capability of accurate classification …