Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

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

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 …

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

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

Recently, graph neural networks (GNNs) have been demonstrated to be a promising
framework in investigating non-Euclidean dependency in hyperspectral (HS) images. Since …

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 …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

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

One of the most important tasks in the advanced transportation systems is road extraction.
Extracting road region from high-resolution remote sensing imagery is challenging due to …