Recent Developments in Endmember Extraction and Spectral Unmixing

  • Antonio Plaza
  • Gabriel Martín
  • Javier Plaza
  • Maciel Zortea
  • Sergio Sánchez
Chapter
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)

Abstract

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called endmembers, and the material fractions, called fractional abundances, at each pixel of the scene. In this chapter, we provide an overview of existing techniques for spectral unmixing and endmember extraction, with particular attention paid to recent advances in the field such as the incorporation of spatial information into the endmember searching process, or the use of nonlinear mixture models for fractional abundance characterization. In order to substantiate the methods presented throughout the chapter, highly representative hyperspectral scenes obtained by different imaging spectrometers are used to provide a quantitative and comparative algorithm assessment. To address the computational requirements introduced by hyperspectral imaging algorithms, the chapter also includes a parallel processing example in which the performance of a spectral unmixing chain (made up of spatial–spectral endmember extraction followed by linear spectral unmixing) is accelerated by taking advantage of a low-cost commodity graphics co-processor (GPU). Combined, these parts are intended to provide a snapshot of recent developments in endmember extraction and spectral unmixing, and also to offer a thoughtful perspective on future potentials and emerging challenges in designing and implementing efficient hyperspectral imaging algorithms.

Keywords

Hyperspectral imaging Spectral unmixing Endmember extraction Neural networks Intelligent training Parallel processing GPUs 

Notes

Acknowledgements

This work has been supported by the European Community’s Marie Curie Research Training Networks Programme under reference MRTN-CT-2006-035927, Hyperspectral Imaging Network (HYPER-I-NET). This work has also been supported by the Spanish Ministry of Science and Innovation (HYPERCOMP/EODIX project, reference AYA2008-05965-C04-02). Gabriel Martín and Sergio Sánchez are sponsored by research fellowships with references BES-2009-017737 and PTA2009-2611-P, respectively, both associated to the aforementioned project. Funding from Junta de Extremadura (local government) under project PRI09A110 is also gratefully acknowledged. The authors thank Andreas Mueller for his lead of the DLR project that allowed us to obtain the DAIS 7915 and ROSIS hyperspectral datasets over Dehesa areas in Extremadura, Spain, and Robert O. Green at NASA/JPL for making the AVIRIS Cuprite scene available to the scientific community. Last but not least, the authors would like to take this opportunity to gratefully acknowledge the Editors of this volume for their very kind invitation to contribute a chapter on the topic of endmember extraction and spectral unmixing, and for all their support and encouragement during all the stages of the production process for this monograph.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Plaza
    • 1
  • Gabriel Martín
    • 1
  • Javier Plaza
    • 1
  • Maciel Zortea
    • 2
  • Sergio Sánchez
    • 1
  1. 1.Department of Technology of Computers and CommunicationsUniversity of ExtremaduraCaceresSpain
  2. 2.Department of Mathematics and StatisticsUniversity of TromsoTromsoNorway

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