Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …

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

Recently, vision transformer (ViT)-based deep learning (DL) models have achieved
remarkable performance gains in hyperspectral image classification (HSIC) due to their …

Transformer has been widely used in classification tasks for hyperspectral images (HSIs) in
recent years. Because it can mine spectral sequence information to establish long-range …

Transformers have proven effective for hyperspectral image classification (HSIC) but often
incorporate average pooling that results in information loss. This letter presents …

Convolutional models have provided outstanding performance in the analysis of
hyperspectral images (HSIs). These architectures are carefully designed to extract intricate …

The transformer model encounters challenges with variable-length input sequences, leading
to efficiency and scalability concerns. To overcome this, we propose a pyramid-based …

Hyperspectral image (HSI) classification has attracted significant interest among researchers
owing to its diverse practical applications. Convolutional neural networks (CNNs) have been …

The profound impact of deep learning and particularly of convolutional neural networks
(CNNs) in automatic image processing has been decisive for the progress and evolution of …

Efficient extraction of spectral sequences and geospatial information is crucial in
hyperspectral image (HSI) classification. Recurrent neural networks (RNNs) and …