Chapter 11. Kernel Methods for Unmixing Hyperspectral Imagery

  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. Joshua Broadwater,
  2. Amit Banerjee and
  3. Philippe Burlina

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch11

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Broadwater, J., Banerjee, A. and Burlina, P. (2009) Kernel Methods for Unmixing Hyperspectral Imagery, 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.ch11

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. Applied Physics Laboratory, The Johns Hopkins University, USA

Publication History

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

ISBN Information

Print ISBN: 9780470722114

Online ISBN: 9780470748992

SEARCH

Keywords:

  • function approximation and regression;
  • kernel methods for unmixing hyperspectral imagery;
  • concept of kernel unmixing algorithms for hyperspectral imagery;
  • hyperspectral sensors - unique data for remote sensing applications;
  • algorithms for unmixing hyperspectral imagery;
  • proposed kernel unmixing algorithm;
  • kernel fully constrained least squares (KFCLS) algorithm;
  • SVDD, KFCLS and rate-distortion estimate;
  • AVIRIS sensor and AVIRIS data results;
  • physics-based kernel results

Summary

This chapter contains sections titled:

  • Introduction

  • Mixing models

  • Proposed kernel unmixing algorithm

  • Experimental results of the kernel unmixing algorithm

  • Development of physics-based kernels for unmixing

  • Physics-based kernel results

  • Summary

  • References