A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …

Texture measurements quantitatively describe relationships of DN values of neighbouring
pixels. The output is a continuous measure of spatial information that may be used for further …

Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image
(HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is …

Recent decades have seen a significant increase in the frequency, intensity, and impact of
natural disasters and other emergencies, forcing the governments around the world to make …

The hyperspectral imaging technology discussed here captures a scene by using various
imaging spectrometer sensors [eg Airborne Visible Infrared Imaging Spectrometer (AVIRIS) …

The high dimensionality of hyperspectral imagery often introduces challenge for the
conventional data analysis techniques. In order to improve the classification performance of …

The quality of an image affects its utility and image quality assessment has been a hot
research topic for many years. One widely used measure for image quality assessment is …

In recent years, deep learning methods have been widely used for the classification of
hyperspectral images (HSIs). However, the training of deep models is very time-consuming …

Hyperspectral image having huge numbers of narrow and contiguous bands involves high
computation complexity in processing and analysing the image. Hence dimensionality …

Hyperspectral images provide fine details of the scene under analysis in terms of spectral
information. This is due to the presence of contiguous bands that make possible to …