Various machine learning and deep learning techniques have been proposed for
classification purposes in the case of hyperspectral imaging. Among all the machine …

Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many
methods have been continuously proposed in recent years. However, it is still challenging to …

Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

The profound impact of deep learning and particularly of convolutional neural networks
(CNNs) in automatic image processing has been decisive for the progress and evolution of …

With the continuous progress of computer deep learning technology, convolutional neural
network (CNN), as a representative approach, provides a unique solution for hyperspectral …

Convolutional neural network (CNN) has received considerable interest in hyperspectral
image classification (HSIC) lately due to its excellent spectral–spatial feature extraction …

Advanced Hyperspectral image sensors can capture high-resolution land cover images.
Many supervised Machine learning (ML) and Deep learning (DL) algorithms succeeded in …

In order to effectively extract features and improve classification accuracy for hyperspectral
remote sensing images (HRSIs), the advantages of enhanced particle swarm optimization …

Semantic segmentation with deep learning networks has become an important approach to
the extraction of objects from very high-resolution remote sensing images. Vision …

Deep learning has achieved good performance in hyperspectral image classification (HSIC).
Many methods based on deep learning use deep and complex network structures to extract …