The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral
imagery has led to significant improvements in terms of classification performance. The task …

Deep convolutional neural networks (CNNs) have shown their outstanding performance in
the hyperspectral image (HSI) classification. The success of CNN-based HSI classification …

The production of a high spatial resolution (HR) hyperspectral image (HSI) through the
fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has …

Hyperspectral image (HSI) classification, which aims to assign an accurate label for
hyperspectral pixels, has drawn great interest in recent years. Although low-rank …

The problem of dense semantic labeling consists in assigning semantic labels to every pixel
in an image. In the context of aerial image analysis, it is particularly important to yield high …

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 …

Band selection refers to the process of choosing the most relevant bands in a hyperspectral
image. By selecting a limited number of optimal bands, we aim at speeding up model …

Taking into account the limitations of optical imaging, image acquisition equipment is usually
designed to make a trade-off between spatial information and spectral information …

In this paper, to break the limit of the traditional linear models for synthetic aperture radar
(SAR) image despeckling, we propose a novel deep learning approach by learning a non …

Air pollution is one of major environmental issues in the twenty-first century due to global
industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts …