Superpixels can define meaningful local regions within a hyperspectral image (HSI) and
have become the building blocks of various HSI classification methods. The superpixels in …

The enormous data and computational resources required by Convolutional Neural
Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive …

Convolutional neural networks (CNNs) exhibit excellent performance in hyperspectral image
classification (HSIC) and have attracted significant interest. Nevertheless, the common CNN …

Hyperspectral image (HSI) data has a wide range of valuable spectral information for
numerous tasks. HSI data encounters challenges such as small training samples, scarcity …

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have
made considerable advances in hyperspectral image (HSI) classification. However, most …

As an effective strategy for reducing the noisy and redundant information for hyperspectral
imagery (HSI), hyperspectral band selection intends to select a subset of original …

The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank
representation (LRR) model to separate the background and anomaly components, where …

Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively
classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled …

The ability of vision transformers (ViTs) to accurately model global dependencies has
completely changed the field of vision research. However, because of their drawbacks, such …

For hyperspectral image classification (HSIC), labeling samples is challenging and
expensive due to high dimensionality and massive data, which limits the accuracy and …