Chapter 15. KPCA Algorithm for Hyperspectral Target/Anomaly Detection

  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. Yanfeng Gu

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch15

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Gu, Y. (2009) KPCA Algorithm for Hyperspectral Target/Anomaly Detection, 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.ch15

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. College of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, P.R. China

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:

  • KPCA algorithm for hyperspectral target/anomaly detection;
  • target and anomaly detection - hyperspectral imagery applications;
  • hyperspectral images in civilian and military fields;
  • hyperspectral images for numerical experiments;
  • kernel-based feature extraction in hyperspectral images;
  • kernel-based target detection in hyperspectral images;
  • KISD algorithm and processing flowchart;
  • kernel-based anomaly detection in hyperspectral images;
  • curve of average local singularity and sequence number of nonlinear principal components;
  • kernel methods motivation for target and anomaly detection in hyperspectral images

Summary

This chapter contains sections titled:

  • Introduction

  • Motivation

  • Kernel-based feature extraction in hyperspectral images

  • Kernel-based target detection in hyperspectral images

  • Kernel-based anomaly detection in hyperspectral images

  • Conclusions

  • Acknowledgments

  • References