Chapter 12. Kernel-Based Quantitative Remote Sensing Inversion

  1. Dr Gustavo Camps-Valls B.Sc., Ph.D. professor member3 and
  2. Dr Lorenzo Bruzzone M.S., Ph.D. Postdoctoral Researcher Professor member Chair4
  1. Yanfei Wang1,
  2. Changchun Yang1 and
  3. Xiaowen Li2

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch12

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Wang, Y., Yang, C. and Li, X. (2009) Kernel-Based Quantitative Remote Sensing Inversion, 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.ch12

Editor Information

  1. 3

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

  2. 4

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

Author Information

  1. 1

    Key Laboratory of Petroleum Geophysics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, P.R. China

  2. 2

    Research Center for Remote Sensing and GIS, Beijing Normal University, Beijing, 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:

  • kernel-based quantitative remote sensing inversion;
  • structural parameters and spectral component signatures of Earth surface cover type;
  • kernel-based operator equations;
  • International Geosphere-Biosphere Programme (IGBP) and remote sensing inversion;
  • typical kernel-based remote sensing inverse problems;
  • well-posedness and ill-posedness;
  • function approximation and regression;
  • numerically truncated singular value decomposition (NTSVD);
  • Newton-type methods - based on Gauss–Newton method and variations;
  • aerosol particle size distribution function retrieval

Summary

This chapter contains sections titled:

  • Introduction

  • Typical kernel-based remote sensing inverse problems

  • Well-posedness and ill-posedness

  • Regularization

  • Optimization techniques

  • Kernel-based BRDF model inversion

  • Aerosol particle size distribution function retrieval

  • Conclusion

  • Acknowledgments

  • References