With the rapid development of deep learning, many deep learning-based approaches have
made great achievements in object detection tasks. It is generally known that deep learning …

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

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 …

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras
and LiDAR, have significant potential in precision agriculture, including object detection …

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

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 …

In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …

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 …

With fast increase in volume of mobile multimedia data, how to apply powerful deep learning
methods to process data with real-time response becomes a major issue. Meanwhile, edge …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …