Images are a convenient way for humans to obtain information and knowledge, but they are
often destroyed throughout the collection or distribution process. Therefore, image …

The neurocomputing communities have focused much interest on quaternionic-valued
neural networks (QVNNs) due to the natural extension in quaternionic signals, learning of …

Quaternion convolutional neural networks (QCNNs) can capture quaternion features, which
contain not only the contextual information among quaternion feature units but also utilize …

Deep learning has dominated hyperspectral image (HSI) classification due to its modular
design and powerful feature extraction capabilities. Recently, a modern macro-architecture …

In recent years, convolutional neural networks (CNNs) have been widely used in
hyperspectral image (HSI) classification because of their exceptional performance in local …

The vision transformer (ViT) has become a hot topic in image processing due to its global
feature extraction capabilities. However, the ViT suffers from over-smoothing in feature …

Deep learning methods have shown great promise in automatically extracting features from
hyperspectral images (HSIs) for classification purposes. Recently, researchers have …

The spectral anomalies, limited training samples, and noisy training labels pose significant
challenges to accurately classifying hyperspectral images (HSIs). To address these issues …

Deep learning has made significant progress in hyperspectral image (HSI) classification,
and its powerful ability to automatically learn abstract features is well recognized. Recently …

Translational equivariance, one of the properties of Convolutional neural networks (CNNs),
directly reflects the coherence of the influence of input at each position on the output. By …