More recently, vision transformer (ViT) has shown competitive performance with
convolutional neural network (CNN) on computer vision tasks, which provided more …

In Transformer-based hyperspectral image classification (HSIC), predefined positional
encodings (PEs) are crucial for capturing the order of each input token. However, their …

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

In hyperspectral images (HSIs), both local and nonlocal features play crucial roles in
classification tasks. Vision transformers (VITs) can extract nonlocal features through …

Due to its capacity to recognize detailed spectral differences, hyperspectral (HS) data have
been extensively used for precise land use land cover (LULC) mapping. However, recent …

The transformer has become a prominent technique for hyperspectral image (HSI)
classification, attributed to its capability to model global dependencies between features …

The integration of multi-source remote sensing data, bolstered by advancements in deep
learning, has emerged as a pivotal strategy for enhancing land use and land cover (LULC) …

Over last decade, hundreds of deep learning algorithms using CNN, ViT, MLP, and deep
LSTM are proposed to classify hyperspectral remote sensing images with accuracy reaching …

The innovation of transformer architecture has propelled the growth of Natural Language
algorithms and models, spanning language models, large language models, and pre …

Abstract Recently, Convolutional Transformer-based models have become popular in
hyperspectral image (HSI) classification tasks and gained competitive classification …