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 …

Hyperspectral image classification has been a vibrant area of research in recent years.
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Hyperspectral image (HSI) clustering is an extremely fundamental but challenging task with
no labeled samples. Deep clustering methods have attracted increasing attention and have …

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 …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

In the literature of remote sensing, deep models with multiple layers have demonstrated their
potentials in learning the abstract and invariant features for better representation and …

Land cover classification in remote sensing is often faced with the challenge of limited
ground truth labels. Incorporating historical ground information has the potential to …

The integration of spatial context in the classification of hyperspectral images is known to be
an effective way in improving classification accuracy. In this paper, a novel spectral-spatial …

Deep convolutional neural networks have been dominating in the field of hyperspectral
image (HSI) classification. However, single convolutional kernel can limit the receptive field …