Classification algorithms rely heavily on feature selection (FS) for accuracy and
performance, and this is a significant research topic. Filter feature selection algorithms are …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI)
classification. However, the fixed receptive field of CNN-based methods limits their capability …

Hyperspectral image (HSI) in Internet of Things (IoT) is a typical small sample data set,
which is difficult and costly to label samples manually. In the feature extraction, it is difficult to …

Hyperspectral image (HSI) classification with limited training samples has been well studied
in recent years. Among them, the few-shot learning (FSL) technique demonstrates excellent …

Hyperspectral image (HSI) classification aims at assigning a unique label for every pixel to
identify categories of different land covers. Existing deep learning models for HSIs are …

The Transformer–convolutional neural network (CNN) hybrid learning approach is gaining
traction for balancing deep and shallow image features for hierarchical semantic …

Hyperspectral image (HSI) classification is a hot topic in remote sensing (RS). A large
number of studies have been proposed and achieved excellent performance. Most of them …

In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise
hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are …

Recently, hyperspectral image (HSI) classification has become a promising research
direction in remote sensing image processing. Many HSI classification methods have been …