Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …

Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many
methods have been continuously proposed in recent years. However, it is still challenging to …

Traditional class imbalanced learning algorithms require training data to be labeled,
whereas semi-supervised learning algorithms assume that the class distribution is balanced …

With the continuous progress of computer deep learning technology, convolutional neural
network (CNN), as a representative approach, provides a unique solution for hyperspectral …

Hyperspectral band selection, which aims to select a small number of bands to reduce data
redundancy and noisy bands, has attracted widespread attention in recent years. Many …

Recent advances in airborne and space-based remote sensing technologies and a rapid
increase in the use of machine learning (ML) techniques in digital image processing …

Hyperspectral band selection plays a crucial role in reducing dimensionality, extracting
relevant features, and improving computational efficiency in hyperspectral data analysis …

Hyperspectral images (HSI) suffer from limited labeled data and the curse of dimensionality,
which makes it difficult to classify imbalanced and small-sized HSI data. To address the …

Recent research shows that the Rotation Forest and its several variants can achieve better
performance than other widely used ensemble methods in classification issues. However, it …

The accurate and timely monitoring of land cover types is of great significance for the
scientific planning, rational utilization, effective protection and management of land …