Construction of Fast and Robust N-FINDR Algorithm

  • Liguo Wang
  • Xiuping Jia
  • Ye Zhang
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)

Abstract

N-FINDR has been a popular algorithm of endmember (EM) extraction method for its fully automation and relative efficiency. Unfortunately, innumerable volume calculation leads to a low speed of the algorithm and so becomes a limitation to its applications. Additionally, the algorithm is vulnerable to outliers that widely exist in hyperspectral data. In this paper, distance measure is adopted in place of volume one to speed up the algorithm and outliers are effectively controlled to endow the algorithm with robustness. Experiments show the improved algorithm is very fast and robust.

Keywords

Hyperspectral Image Hyperspectral Data Mixed Pixel Fractional Abundance Distance Comparison 
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.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liguo Wang
    • 1
    • 2
  • Xiuping Jia
    • 3
  • Ye Zhang
    • 2
  1. 1.School of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  2. 2.Dept. of Information EngineeringHarbin Institute of TechnologyHarbinChina
  3. 3.School of Electrical EngineeringUniversity College, the University of New SouthWales, Australian Defence Force AcademyCampbellAustralia

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