The proportion of instances belonging to each class in a data-set plays an important role in
machine learning. However, the real world data often suffer from class imbalance. Dealing …

Recently, there has been growing interest in hyperspectral images (HSIs) classification
tasks, with both graph neural networks (GNN) and convolutional neural networks (CNNs) …

Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance,
which is considered to be one of the major challenges in the field of remote sensing. In …

Diversity within base classifiers has been recognized as an important characteristic of an
ensemble classifier. Data and feature sampling are two popular methods of increasing such …

In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …

Rotation forest (RoF) is a powerful ensemble classifier and has been demonstrated the
outstanding performance in hyperspectral data classification. However, the classification …

Recently, vision transformer (ViT)-based deep learning (DL) models have achieved
remarkable performance gains in hyperspectral image classification (HSIC) due to their …

Rotation forest (RoF) is a powerful ensemble classifier and has attracted substantial
attention due to its performance in hyperspectral data classification. Multi-class imbalance …

Conventional classification algorithms have shown great success in balanced hyperspectral
data classification. However, the imbalanced class distribution is a fundamental problem of …

Real-world datasets are often contaminated with label noise; labeling is not a clear-cut
process and reliable methods tend to be expensive or time-consuming. Depending on the …