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 …

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 Image (HSI) classification methods that use Deep Learning (DL) have proven
to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have …

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

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of
remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) …

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

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

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

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …