In this paper, we propose two novel classification frameworks for hyperspectral image (HSI)
based on capsule network (CapsNet), which could address the drawbacks of convolutional …

Deep learning methods have shown their marvel performance on hyperspectral image (HSI)
classification tasks. In particular, algorithms based on convolution neural network (CNN) …

Hyperspectral image (HSI) classification is a hot topic in remote sensing community; many
researchers have made a great deal of effort in this domain. Recently, deep learning‐based …

The combined use of spatial information and spectral information has been widely applied to
hyperspectral image (HSI) classification. In recent years, multiscale spatial-spectral …

Recently, deep learning-based algorithms have been widely used for classification of
hyperspectral images (HSIs) by extracting invariant and abstract features. In our conference …

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral
information. And this is an advantage for the common remotely sensed data. Thus, the …

With the development of deep learning, various convolutional neural network (CNN)-based
methods have been proposed for the hyperspectral image (HSI) classification. Although …

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

Recently, convolutional neural networks (CNNs) have been widely applied to hyperspectral
image (HSI) classification due to their detailed representation of features. Nevertheless, the …

Deep learning models have shown excellent performance in the hyperspectral remote
sensing image (HSI) classification. In particular, convolutional neural networks (CNNs) have …