Self-Organizing Map for Hyperspectral Image Analysis

  • P. Martinez
  • P. L. Aguilar
  • R. M. Pérez
  • M. Linaje
  • J. C. Preciado
  • A. Plaza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)


In this paper we present a neural network methodology used for classifying an hyperspectral image referencied as Indian Pines. The network parameters (learning and neighborhood function) are adjusted using a test battery generated from the image, selecting the values that give the best robutness and discrimination capacity. The availity of ground truth allows us to intriduce a new stadistical measure to quantify the resulting classification accuracy. The results of this methodology show an accuracy of 80% in the classification.


Output Layer Confusion Matrix Hyperspectral Image Hyperspectral Data Neighborhood Function 
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 2001

Authors and Affiliations

  • P. Martinez
    • 1
  • P. L. Aguilar
    • 1
  • R. M. Pérez
    • 1
  • M. Linaje
    • 1
  • J. C. Preciado
    • 1
  • A. Plaza
    • 1
  1. 1.Departamento de InformáticaUniversidad de Extremadura, Avda. de la Universidad s/nCáceresSPAIN

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