In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

The recent success of attention mechanism-driven deep models, like vision transformer (ViT)
as one of the most representatives, has intrigued a wave of advanced research to explore …

The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …

Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …

In recent years, enormous research has been made to improve the classification
performance of single-modal remote sensing (RS) data. However, with the ever-growing …

Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …

Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …