Change detection is one of the most important applications of remote sensing technology. It
is a challenging task due to the obvious variations in the radiometric value of spectral …

Change detection is one of the most important open topics for multi-temporal remote sensing
technology to observe the earth. Recently, many methods are proposed to detect the land …

Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently
spectrally unmix the data, three key steps must be accomplished: Estimate the number of …

The Gough Map, one of the earliest surviving maps of Britain, was created and extensively
revised over the 15th century. In 2015, the map was imaged using a hyperspectral imaging …

Combining multiple satellite remote sensing sources provides a far richer, more frequent
view of the earth than that of any single source; the challenge is in distilling these petabytes …

In hyperspectral target detection, one must contend with variability in both target materials
and background clutter. While most algorithms focus on the background clutter, there are …

Spectral image complexity is an ill-defined term that has been addressed previously in terms
of dimensionality, multivariate normality, and other approaches. Here, we apply the concept …

Most spectral image processing schemes develop models of the data in the hyperspace by
using first and second order statistics or linear subspace geometries applied to the image …

In this research, a spectral unmixing and classification approach for hyperspectral imagery
(HSI) of historical artifacts is introduced. The Gram Matrix and Max-D techniques have been …

Many techniques from graph theory and network theory have been applied to traditional
images, and some techniques are now being applied to spectral imagery. Contrary to the …