Limited training data, high dimensionality, image complexity, and similarity between classes
are challenges confronting hyperspectral image (HSI) classification often resulting in …

Deep learning techniques have boosted the performance of hyperspectral image (HSI)
classification. In particular, convolutional neural networks (CNNs) have shown superior …

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

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

Hyperspectral imaging is a highly advanced and sophisticated method for capturing images
in hundreds of narrow, contiguous spectral bands. However, processing and analyzing such …

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 …

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

Convolutional neural networks (CNNs) have recently exhibited an excellent performance in
hyperspectral image classification tasks. However, the straightforward CNN-based network …

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

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