In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …

Hyperspectral image (HSI) classification has attracted wide attention in many fields.
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …

Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …

This study proposes a novel learning scheme for the kernel extreme learning machine
(KELM) based on the chaotic moth-flame optimization (CMFO) strategy. In the proposed …

As one of the fundamental research topics in remote sensing image analysis, hyperspectral
image (HSI) classification has been extensively studied so far. However, how to …

Since its introduction, kernel extreme learning machine (KELM) has been widely used in a
number of areas. The parameters in the model have an important influence on the …

It is of great interest in exploiting texture information for classification of hyperspectral
imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich …

Hyperspectral image (HSI) classification is an active and important research task driven by
many practical applications. To leverage deep learning models especially convolutional …

The classification problems with imbalanced datasets widely exist in real word. An Extreme
Learning Machine is found unsuitable for imbalanced classification problems. This work …