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

Semisupervised deep learning methods (DLMs) can mitigate the dependence on large
amounts of labeled samples using a small number of labeled samples. However, for …

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 …

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 …

The challenges in hyperspectral image (HSI) classification lie in the existence of noisy
spectral information and lack of contextual information among pixels. Considering the three …

Classification of hyperspectral image (HSI) is an important research topic in the remote
sensing community. Significant efforts (eg, deep learning) have been concentrated on this …

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

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 …

Hyperspectral image (HSI) classification is a focus area in remote sensing research, wherein
redundant spectral information poses a significant challenge and deep-learning-based …