Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from
being a sparse research tool into a commodity product available to a broad user community. As a result, there is
an emerging need for standardized data processing techniques, able to take into account the special properties of
hyperspectral data and to take advantage of latest-generation sensor instruments and computing environments.
The goal of this paper is to provide a seminal view on recent advances in techniques for hyperspectral data
classification. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the
data, and to integrate the spatial and spectral information. The performance of the proposed techniques is evaluated
in different analysis scenarios, including land-cover classification, urban mapping and spectral unmixing.
To satisfy time-critical constraints in many remote sensing applications, parallel implementations for some of
the discussed algorithms are also developed. Combined, these parts provide a snapshot of the state-of-the-art in
those areas, and offer a thoughtful perspective on the potential and emerging challenges in the design of robust
hyperspectral data classification algorithms.