Computing for analysis and modeling of hyperspectral imagery

Gregory P. Asner, Robert S. Haxo, David E. Knapp

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Hyperspectral remote sensing is increasingly used for Earth observation and analysis, but the large data volumes and complex analytical techniques associated with imaging spectroscopy require high-performance computing approaches. In this chapter, we highlight several analytical methods employed in vegetation and ecosystem studies using airborne and space-based imaging spectroscopy. We then summarize the most common high-performance computing approaches used to meet these analytical demands, and provide examples from our own work with computing clusters. Finally, we discuss several emerging areas of high-performance computing, including data processing onboard aircraft and spacecraft and distributed Internet computing, that will change the way we carry out computations with high spatial and spectral resolution observations of ecosystems.

Original languageEnglish (US)
Title of host publicationHigh Performance Computing in Remote Sensing
PublisherCRC Press
Pages109-130
Number of pages22
ISBN (Electronic)9781420011616
ISBN (Print)9781584886624
StatePublished - Jan 1 2007
Externally publishedYes

    Fingerprint

ASJC Scopus subject areas

  • Engineering(all)
  • Earth and Planetary Sciences(all)
  • Mathematics(all)
  • Computer Science(all)

Cite this

Asner, G. P., Haxo, R. S., & Knapp, D. E. (2007). Computing for analysis and modeling of hyperspectral imagery. In High Performance Computing in Remote Sensing (pp. 109-130). CRC Press.