AMEEPAR: Parallel Morphological Algorithm for Hyperspectral Image Classification on Heterogeneous Networks of Workstations

  • Antonio Plaza
  • Javier Plaza
  • David Valencia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images corresponding to different wavelength channels for the same area on the surface of the Earth. Most available techniques for hyperspectral image classification focus on analyzing the data without incorporating the spatial information; i.e. the data is treated not as an image but as an unordered listing of spectral measurements where the spatial coordinates can be shuffled arbitrarily without affecting the final analysis. Despite the growing interest in the development of techniques for interpretation and classification of such high-dimensional imagery, only a few efforts devoted to the design of parallel implementations exist in the open literature. In this paper, we describe AMEEPAR, a parallel morphological algorithm that integrates the spatial and spectral information. The algorithm has been specifically optimized in this work for execution on heterogeneous networks of workstations. The parallel properties and classification accuracy of the proposed approach are evaluated using four networks of workstations distributed among different locations, and a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center.


Heterogeneous Network Message Passing Interface Hyperspectral Image Homogeneous Network Image Cube 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Green, R.O., et al.: Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment 65, 227–248 (1998)CrossRefGoogle Scholar
  2. 2.
    Lastovetsky, A., Reddy, R.: On Performance Analysis of Heterogeneous Parallel Algorithms. Parallel Computing 30, 1195–1216 (2004)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Plaza, A., Martinez, P., Perez, R., Plaza, J.: Spatial/Spectral Endmember Extraction by Multidimensional Morphological Operations. IEEE Transactions on Geoscience and Remote Sensing 9, 2025–2041 (2002)CrossRefGoogle Scholar
  4. 4.
    Seinstra, F.J., Koelma, D., Geusebroek, J.M.: A Software Architecture for User Transparent Parallel Image Processing. Parallel Computing 28, 967–993 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Plaza, A., Valencia, D., Plaza, J.: Commodity Cluster-Based Parallel Processing of Hyperspectral Imagery. Journal of Parallel and Distributed Computing 66, 345–358 (2006)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antonio Plaza
    • 1
  • Javier Plaza
    • 1
  • David Valencia
    • 1
  1. 1.Department of Computer ScienceUniversity of ExtremaduraCaceresSpain

Personalised recommendations