Supervised hyperspectral image (HSI) classification has been widely studied and used in
many different applications. However, the performance of the supervised classifiers …

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

Owing to the powerful data representation ability of deep learning (DL) techniques,
tremendous progress has been recently made in hyperspectral image (HSI) classification …

In recent years, with the continuous development of deep learning (DL), neural networks
have demonstrated good results in large-sample hyperspectral image (HSI) classification …

Deep learning approaches have recently been widely applied to the classification of
hyperspectral images (HSIs) and achieve good capability. Deep learning can effectively …

With the development of deep neural networks, hyperspectral image (HSI) classification
systems have achieved a significant improvement. These systems require numerous and …

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

In the hyperspectral image (HSI) classification, convolutional neural network (CNN)-based
approaches often struggle with the scarcity of labeled samples. The letter proposes an HSI …

Deep neural networks (DNNs) have underpinned most of recent progress of hyperspectral
image (HSI) classification. One premise of their success lies in the high image quality …