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

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

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 …

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 …

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 learning models, especially deep convolutional neural networks (CNNs), have been
intensively investigated for hyperspectral image (HSI) classification due to their powerful …

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

This paper studies the classification problem of hyperspectral image (HSI). Inspired by the
great success of deep neural networks in Artificial Intelligence (AI), researchers have …

Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a
proper land-cover label. Recently, convolutional neural networks (CNNs) have shown …

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