Chapter 3. The Support Vector Machine (SVM) Algorithm for Supervised Classification of Hyperspectral Remote Sensing Data

  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. J. Anthony Gualtieri

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch3

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Gualtieri, J. A. (2009) The Support Vector Machine (SVM) Algorithm for Supervised Classification of Hyperspectral Remote Sensing Data, 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.ch3

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. NASA/GSFC, CISTO & Global Science and Technology, 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:

  • supervised image classification;
  • Support Vector Machine(SVM) algorithm and supervised classification of hyperspectral remote sensing data;
  • remote sensing with imaging spectrometers;
  • AVIRIS - acronym for NASA Airborne Visible InfraRed Imaging Spectrometer;
  • hyperspectral data and its acquisition;
  • raw AVIRIS image to geo-corrected AVIRIS image;
  • Signal-to-Noise Ratio (SNR) of sensor;
  • hyperspectral remote sensing and supervised classification;
  • mathematical foundations of supervised classification;
  • SVM results for Gaussian kernel (RBF) and polynomial kernel

Summary

This chapter contains sections titled:

  • Introduction

  • Aspects of hyperspectral data and its acquisition

  • Hyperspectral remote sensing and supervised classification

  • Mathematical foundations of supervised classification

  • From structural risk minimization to a support vector machine algorithm

  • Benchmark hyperspectral data sets

  • Results

  • Using spatial coherence

  • Why do SVMs perform better than other methods?

  • Conclusions

  • References