Special Section on High-Performance Computing in Applied Remote Sensing

Parallel hyperspectral image processing on distributed multicluster systems

[+] Author Affiliations
Fangbin Liu

Universiteit van Amsterdam, Informatics Institute, Science Park 107, 1098 XG Amsterdam, The Netherlands

Frank J. Seinstra

Vrije Universiteit, Department of Computer Science, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands

Antonio Plaza

University of Extemadura, Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications,Avenida de la Universidad s/n, E-10003 Caceres, Spain

J. Appl. Remote Sens. 5(1), 051501 (November 18, 2011). doi:10.1117/1.3595292
History: Received February 01, 2011; Revised April 01, 2011; Accepted May 03, 2011; Published November 18, 2011; Online November 18, 2011
Text Size: A A A

Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of emerging problems in hyperspectral imaging, there is a growing need for distributed supercomputing using multicluster systems. A well-known manner of obtaining speedups in hyperspectral imaging is to apply data parallel approaches, in which commonly used data structures (e.g., the image cubes) are being scattered among the available compute nodes. Such approaches work well for individual compute clusters, but—due to the inherently large wide-area communication overheads—these are generally not applied in distributed multi-cluster systems. Given the nature of many algorithmic approaches in hyperspectral imaging, however, and due to the increasing availability of high-bandwidth optical networks, wide-area data parallel execution may well be a feasible acceleration approach. This paper discusses the wide-area data parallel execution of two realistic and state-of-the-art algorithms for endmember extraction in hyperspectral unmixing applications: automatic morphological endmember extraction and orthogonal subspace projection. It presents experimental results obtained on a real-world multicluster system, and provides a feasibility analysis of the applied parallelization approaches. The two parallel algorithms evaluated in this work had been developed before for single-cluster execution, and were not changed. Because no further implementation efforts were required, the proposed methodology is easy to apply to already available algorithms, thus reducing complexity and enhancing standardization.

Figures in this Article
© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)


Fangbin Liu ; Frank J. Seinstra and Antonio Plaza
"Parallel hyperspectral image processing on distributed multicluster systems", J. Appl. Remote Sens. 5(1), 051501 (November 18, 2011). ; http://dx.doi.org/10.1117/1.3595292

Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.