Dynamic Learning of SCRF for Feature Selection and Classification of Hyperspectral Imagery

  • Ping Zhong
  • Zhiming Qian
  • Runsheng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

This paper investigates the feature selection and contextual classification of hyperspectral images through the sparse conditional random field (SCRF) model. To relieve the heavy degeneration of classification performance caused by the characteristics of the hyperspectral data and the oversparsity when SCRF selects a small feature subset, we develop a dynamic learning framework to train the SCRF. Under the piecewise training framework, the proposed dynamic learning method of SCRF can be implemented efficiently through separated dynamic sparse trainings of simple classifiers defined by corresponding potentials. Experiments on the real-world hyperspectral images attest to the effectiveness of the proposed method.

Keywords

Conditional random field classification feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ping Zhong
    • 1
  • Zhiming Qian
    • 1
  • Runsheng Wang
    • 1
  1. 1.ATR National Laboratory, School of Electronic Science and EngineeringNational University of Defense TechnologyChangshaChina

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