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

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

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 …

Deep-learning-based methods are widely used in multisource remote-sensing image
classification, and the improvement in their performance confirms the effectiveness of deep …

Domain adaption (DA) is a challenging task that integrates knowledge from source domain
(SD) to perform data analysis for target domain. Most of the existing DA approaches only …

In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture
for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Joint use of multisensor information has attracted considerable attention in the remote
sensing community. While applications in land-cover observation benefit from information …

Convolutional neural networks (CNNs) have been widely used for hyperspectral image
classification. As a common process, small cubes are first cropped from the hyperspectral …

Transformer architectures have become state-of-the-art models in computer vision and
natural language processing. To a significant degree, their success can be attributed to self …