Spectral mixture analysis provides an efficient mechanism for the interpretation and
classification of remotely sensed multidimensional imagery. It aims to identify a set of …

Machine learning approaches are valuable methods in hyperspectral remote sensing,
especially for the classification of land cover or for the regression of physical parameters …

Spectral mixture analysis (SMA) has been widely utilized to address the mixed-pixel
problem in the quantitative analysis of hyperspectral remote sensing images, in which …

Hyperspectral remote sensing reveals detailed information about the optical response of a
scene. Self-Organizing Maps (SOMs) can partition a hyperspectral dataset into clusters, both …

In this paper, we introduce a framework to solve regression problems based on high-
dimensional and small datasets. This framework involves two self-organizing maps (SOM) …

Spectral unmixing is a key process in identifying spectral signature of materials and
quantifying their spatial distribution over an image. The linear model is expected to provide …

We propose a low false alarm methodology to determine anomalies in hyperspectral data.
The method is based on the assumptions that the linear mixing model is valid and that, due …

In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need
for machine learning techniques which are well-suited for these different datasets. One …

Endmember extraction is usually based on the solution of a system of linear equations that
allows the identification of some basic spectra in terms of which the observed mixed spectra …

We propose a computationally efficient methodology to determine the background classes
and anomalies in a moving sequence of hyperspectral data. A target material may be …