Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

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 …

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

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

Neural networks have dominated the research of hyperspectral image classification,
attributing to the feature learning capacity of convolution operations. However, the fixed …

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 …

In the last five years, deep learning has been introduced to tackle the hyperspectral image
(HSI) classification and demonstrated good performance. In particular, the convolutional …

Developments in space-based hyperspectral sensors, advanced remote sensing, and
machine learning can help crop yield measurement, modelling, prediction, and crop …

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

This article brings together the advances of multisource and multitemporal data fusion
approaches with respect to the various research communities and provides a thorough and …