The existing hyperspectral image (HSI) classification encounters the obstacle of improving
the classification accuracy with limited labeled samples. In this context, as a typical …

In past few years, hyperspectral image classification (HSIC) has been one of the most
sparkling fields of research in the area of remote sensing. The presence of very complex …

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

Recently, the capsule network and its enhanced version named convolutional capsule
network (Conv-CapsNet) were applied to hyperspectral image (HSI) classification and …

The convolution neural network (CNN) methods have achieved excellent performance in
hyperspectral image (HSI) classification. However, the convolution network fails to utilize the …

Abstract Convolutional Neural Network (CNN) has been widely employed in hyperspectral
image (HSI) classification. However, CNN cannot attain the relative location relation of …

High dimensional data in hyperspectral remote sensing leads to computational, analytical,
and storage complexities. Dimensionality reduction serves as an efficient tool to remove …

Models based on capsule neural network (CapsNet), a novel deep learning method, have
recently made great achievements in hyperspectral remote sensing image (HSI) …

Skin cancer is a serious cancer caused by the uncontrollable growth of damaged DNA that
leads to death. It is essential to identify the disease at the initial stage and eliminate it from …

Aiming at the problems that most existing segmentation methods are difficult to deal with the
imbalance of remote sensing image distribution and the overlap of segmentation target …