Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art
performance on many computer vision tasks (eg, object recognition, object detection …

Convolutional neural network (CNN) has demonstrated impressive ability to represent
hyperspectral images and to achieve promising results in hyperspectral image classification …

Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots
in the field of remote sensing. Because HSI's features of integrating image and spectrum …

The development of remote sensing images in recent years has made it possible to identify
materials in inaccessible environments and study natural materials on a large scale. But …

It is widely agreed that spatial features can be combined with spectral properties for
improving interpretation performances on very-high-resolution (VHR) images in urban …

In recent years, deep learning based methods have attracted broad attention in the field of
hyperspectral image classification. However, due to the massive parameters and the …

Hyperspectral imaging is a technique that provides rich chemical or compositional
information not regularly available to traditional imaging modalities such as intensity …

Hyperspectral image (HSI) usually holds information of land cover classes as a set of many
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …

With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …

Hyperspectral imaging (HSI) is a competitive remote sensing technique in several fields,
from Earth observation to health, robotic vision, and quality control. Each HSI scene contains …