Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

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

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 …

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …

Despite the success of deep learning algorithms in hyperspectral image (HSI) classification,
most deep learning models require a large amount of labeled data to optimize the numerous …

Recently, embedding and metric-based few-shot learning (FSL) has been introduced into
hyperspectral image classification (HSIC) and achieved impressive progress. To further …

Convolutional neural networks (CNNs) have been widely used in hyperspectral image
classification (HSIC). Although the current CNN-based methods have achieved good …

The development of remote sensing images in recent years has made it possible to identify
materials in inaccessible environments and study natural materials on a large scale. But …

Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

The emergence of a convolutional neural network (CNN) has greatly promoted the
development of hyperspectral image (HSI) classification technology. However, the …