In the rapidly evolving realm of remote sensing technology, the classification of
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …

In this article, a comprehensive review of the state-of-art graph-based learning methods for
classification of the hyperspectral images (HSI) is provided, including a spectral information …

The application of graph convolutional networks (GCNs) to hyperspectral image (HSI)
classification is a heavily researched topic. However, GCNs are based on spectral filters …

Deep learning methods have recently been successfully explored for hyperspectral image
(HSI) classification. However, training a deep-learning classifier notoriously requires …

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels, which can be used to accurately classify diverse materials of …

Recently, deep learning-based hyperspectral image (HSI) classification techniques are
flourishing and exhibit good performance, where cross-domain information is usually utilized …

Deep learning has achieved great success in hyperspectral image classification. However,
when processing new hyperspectral images, the existing deep learning models must be …

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 …

In the field of hyperspectral image (HSI) classification, deep learning has helped achieve
great successes. However, most of these achievements are made with very large amounts of …

Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the
spectral values of the input channels. However, the spatial context around a pixel is also …