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 …

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 …

Hyperspectral image (HSI) classification is an important task in earth observation missions.
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …

Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …

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 …

Background Artificial intelligence (AI) has served humanity in many applications since its
inception. Currently, it dominates the imaging field—in particular, image classification. The …

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 …

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

Remote-sensing image scene classification can provide significant value, ranging from
forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial …

Deep neural networks have been widely applied to hyperspectral image (HSI) classification
areas, in which recurrent neural network (RNN) is one of the most typical networks. Most of …