Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

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 …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Currently, the different deep neural network (DNN) learning approaches have done much for
the classification of hyperspectral images (HSIs), especially most of them use the …

In recent years, the applications of graph convolutional networks (GCNs) in hyperspectral
image (HSI) classification have attracted much attention. However, hyperspectral …

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 …

With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult
Problem in current research. The graph neural network (GNN) has emerged as an approach …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks,
pointing themselves as the current state-of-the-art of deep learning methods. However, the …

Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed
imagery. In particular, convolutional neural networks (CNNs) are gaining more and more …