Chapter 7. Target Detection with Kernels

  1. Dr Gustavo Camps-Valls B.Sc., Ph.D. professor member2 and
  2. Dr Lorenzo Bruzzone M.S., Ph.D. Postdoctoral Researcher Professor member Chair3
  1. Nasser M. Nasrabadi

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch7

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Nasrabadi, N. M. (2009) Target Detection with Kernels, in Kernel Methods for Remote Sensing Data Analysis (eds G. Camps-Valls and L. Bruzzone), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470748992.ch7

Editor Information

  1. 2

    Image Processing Laboratory (IPL) & Dept. Enginyeria Electrónica, Universitat de Valéncia, Spain

  2. 3

    Dept. Information Engineering and Computer Science, University of Trento, Italy

Author Information

  1. US Army Research Laboratory, USA

Publication History

  1. Published Online: 4 NOV 2009
  2. Published Print: 23 OCT 2009

ISBN Information

Print ISBN: 9780470722114

Online ISBN: 9780470748992

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Keywords:

  • target detection with kernels;
  • hyperspectral imagery - in reconnaissance and surveillance applications;
  • linear techniques and corresponding kernel versions;
  • kernel learning theory;
  • linear subspace-based anomaly detectors and kernel versions;
  • inner-window region (IWR) and outer-window region (OWR);
  • Kernel fisher discriminant analysis;
  • Kernel eigenspace separation transform;
  • Hyperspectral Digital Imagery Collection Experiment (HYDICE) data set;
  • linear and nonlinear subspace-based anomaly methods for hyperspectral target detection

Summary

This chapter contains sections titled:

  • Introduction

  • Kernel learning theory

  • Linear subspace-based anomaly detectors and their kernel versions

  • Results

  • Conclusion

  • References