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GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification

  • AmirAbbas DavariEmail author
  • Vincent Christlein
  • Sulaiman Vesal
  • Andreas Maier
  • Christian Riess
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10425)

Abstract

Severely limited training data is one of the major and most common challenges in the field of hyperspectral remote sensing image classification. Supervised learning on limited training data requires either (a) designing a highly capable classifier that can handle such information scarcity, or (b) designing a highly informative and easily separable feature set. In this paper, we adapt GMM supervectors to hyperspectral remote sensing image features. We evaluate the proposed method on two datasets. In our experiments, inclusion of GMM supervectors leads to a mean classification improvement of about \(4.6\%\).

Keywords

Hyperspectral image classification Remote sensing Limited training data GMM supervector 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • AmirAbbas Davari
    • 1
    Email author
  • Vincent Christlein
    • 1
  • Sulaiman Vesal
    • 1
  • Andreas Maier
    • 1
  • Christian Riess
    • 1
  1. 1.Pattern Recognition Lab, Computer Science DepartmentFriedrich-Alexander-University Erlangen-NurembergErlangenGermany

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