A Clustering Based Method for Edge Detection in Hyperspectral Images

  • V. C. Dinh
  • Raimund Leitner
  • Pavel Paclik
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

Edge detection in hyperspectral images is an intrinsically difficult problem as the gray value intensity images related to single spectral bands may show different edges. The few existing approaches are either based on a straight forward combining of these individual edge images, or on finding the outliers in a region segmentation. As an alternative, we propose a clustering of all image pixels in a feature space constructed by the spatial gradients in the spectral bands. An initial comparative study shows the differences and properties of these approaches and makes clear that the proposal has interesting properties that should be studied further.

Keywords

Edge Detection Hyperspectral Image Spectral Space Edge Detection Method Cluster Base Method 
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 2009

Authors and Affiliations

  • V. C. Dinh
    • 1
    • 2
  • Raimund Leitner
    • 2
  • Pavel Paclik
    • 3
  • Robert P. W. Duin
    • 1
  1. 1.ICT GroupDelft University of TechnologyDelftThe Netherlands
  2. 2.Carinthian Tech Research AGVillachAustria
  3. 3.PR Sys DesignDelftThe Netherlands

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