Fully supervised methods for hyperspectral image (HSI) classification usually require a
considerable number of training samples to obtain high classification accuracy. However, it …

In recent years, deep learning-based methods have exhibited remarkable performance in
the field of hyperspectral image (HSI) classification. However, conventional supervised …

Hyperspectral image (HSI) classification is an active research topic in remote sensing.
Supervised learning-based methods have been widely used in HSI classification tasks due …

Hyperspectral image (HSI) classification is an important topic in the community of remote
sensing, which has a wide range of applications in geoscience. Recently, deep learning …

Deep learning technologies have been successfully applied to hyperspectral (HS) image
classification with remarkable performance. However, compared with traditional machine …

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 …

Deep learning methods have made considerable progress in many fields, but most of them
rely on a large amount of sample. In the hyperspectral image (HSI) classification task, many …

In recent years, convolutional neural networks (CNNs) have achieved great success in
hyperspectral image (HSI) classification attributed to their unparalleled capacity to extract …

While deep learning algorithms have achieved good results in hyperspectral image (HSI)
classification, several supervised classification algorithms rely on a large number of labeled …

Deep learning provides excellent potentials for hyperspectral images (HSIs) classification,
but it is infamous for requiring large amount of labeled samples while the collection of high …