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

Hyperspectral image (HSI) classification is an active and important research task driven by
many practical applications. To leverage deep learning models especially convolutional …

In recent years, deep learning based methods have attracted broad attention in the field of
hyperspectral image classification. However, due to the massive parameters and the …

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

Deep neural networks (DNNs), including convolutional neural networks (CNNs) and
residual networks (ResNets) models, are able to learn abstract representations from the …

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …

Due to its remarkable feature representation capability and high performance, convolutional
neural networks (CNN) have emerged as a popular choice for hyperspectral image (HSI) …