Recent advances in techniques for hyperspectral image processing

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. Currently, there is a need for standardized data processing techniques able to take into account the s...

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Bibliographic Details
Published in:Remote sensing of environment 2009-09, Vol.113 (suppl. 1), p.S110-S122
Main Authors: Plaza, Antonio, Benediktsson, Jon Atli, Boardman, Joseph W., Brazile, Jason, Bruzzone, Lorenzo, Camps-Valls, Gustavo, Chanussot, Jocelyn, Fauvel, Mathieu, Gamba, Paolo, Gualtieri, Anthony, Marconcini, Mattia, Tilton, James C., Trianni, Giovanna
Format: Article
Language:English
Subjects:
Classification
Computer Science
Endmember extraction
Engineering Sciences
Hyperspectral imaging
Kernel methods
Markov random fields
Mathematical morphology
Parallel processing
Signal and Image processing
Spatial/spectral processing
Spectral mixture analysis
Support vector machines
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Summary: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. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. 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. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms.
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2007.07.028