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 …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

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 (CNN) and Graph Neural Networks (GNN), such as Graph
Attention Networks (GAT), are two classic neural network models, which are applied to the …

Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can
capture up to several hundred images of different wavelengths and offer relevant spectral …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Plant diseases affect the growth of their respective species, therefore their early identification
is very important. Many Machine Learning (ML) models have been employed for 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 …

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

Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …