A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …

As a powerful visual model, convolutional neural networks (CNNs) have demonstrated
remarkable performance in various visual recognition problems, and attracted considerable …

Band selection, by choosing a set of representative bands in a hyperspectral image, is an
effective method to reduce the redundant information without compromising the original …

As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …

Density peak clustering (DPC) algorithm has become a well-known clustering method
during the last decade, The research communities believe that DPC is a powerful tool …

Hyperspectral images (HSIs) are a powerful source of reliable data in various remote
sensing applications. But due to the large number of bands, HSI has information …

The precise classification of land covers with hyperspectral imagery (HSI) is a major
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, so the
richness of information has been greatly improved. However, these bands lead to increasing …