Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …

The effective combination of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data can be used for land cover classification. Recently, deep-learning-based …

The fusion of multisource remote sensing (RS) data has demonstrated significant potential in
target recognition and classification tasks. However, there is limited emphasis on capturing …

Rolling bearings are the core components of rotating machinery, and their normal operation
is crucial to entire industrial applications. Most existing condition monitoring methods have …

The successes of attention-driven deep models like the vision transformer (ViT) have
sparked interest in cross-domain exploration. However, current transformer-based …

Masked image modeling (MIM) is a highly popular and effective self-supervised learning
method for image understanding. The existing MIM-based methods mostly focus on spatial …

In recent years, convolutional neural networks (CNNs) have achieved remarkable success
in hyperspectral image (HSI) classification tasks, primarily due to their outstanding spatial …

With the abundant emergence of remote sensing (RS) data sources, multimodal remote
sensing observation has become an active field. Extracting valuable information from …

The joint classification of hyperspectral images (HSIs) and LiDAR data plays a crucial role in
Earth observation missions. Most advanced methods are based on discrete label …

The effective utilization of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data is essential for land cover classification. Recently, deep learning-based …