Non Orthogonal Component Analysis: Application to Anomaly Detection

  • Jean-Michel Gaucel
  • Mireille Guillaume
  • Salah Bourennane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


Independent Component Analysis (ICA) has shown success in blind source separation. Its applications to remotely sensed images have been investigated recently. In this approach, a Linear Spectral Mixture (LSM) model is used to characterize spectral data. This model and the associated linear unmixing algorithms are based on the assumption that the spectrum for a given pixel in an image is a linear combination of the end-member spectra. The assumption that the abundances are mutually statistically independent random sources requires the separating matrix to be unitary. This paper considers a new approach, the Non Orthogonal Component Analysis (NOCA), which enables to relax this assumption. The experimental results demonstrate that the proposed NOCA provides a more effective technique for anomaly detection in hyperspectral imagery than the ICA approach. In particular, we highlight the fact that the difference between the performances of the two approaches increases when the number of bands decreases.


Independant Component Analysis Anomaly Detection Hyperspectral Image Independent Component Analysis Blind Source Separation 
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

  • Jean-Michel Gaucel
    • 1
  • Mireille Guillaume
    • 1
  • Salah Bourennane
    • 1
  1. 1.Institut Fresnel / CNRS UMR 6133 – EGIMMarseilleFrance

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