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Reseach Article

Hyperspectral Image Classification on Decision level fusion

Published on March 2012 by GitanjaliS.Korgaonkar, R.R.Sedamkar, KiranBhandari
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET2012 - Number 7
March 2012
Authors: GitanjaliS.Korgaonkar, R.R.Sedamkar, KiranBhandari
46fc99c1-7c59-4fc2-b6b0-3040d96ad6c9

GitanjaliS.Korgaonkar, R.R.Sedamkar, KiranBhandari . Hyperspectral Image Classification on Decision level fusion. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 7 (March 2012), 1-9.

@article{
author = { GitanjaliS.Korgaonkar, R.R.Sedamkar, KiranBhandari },
title = { Hyperspectral Image Classification on Decision level fusion },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { March 2012 },
volume = { ICWET2012 },
number = { 7 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 1-9 },
numpages = 9,
url = { /proceedings/icwet2012/number7/5358-1049/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A GitanjaliS.Korgaonkar
%A R.R.Sedamkar
%A KiranBhandari
%T Hyperspectral Image Classification on Decision level fusion
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET2012
%N 7
%P 1-9
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper different types of image classification will be studied. Decision level fusion, using a specific criterion or algorithm to integrate the classified results from different classifiers, has shown great benefits to improve classification accuracy of multi-source remote sensing images. Based on a survey to hyperspectral remote sensing classification techniques and decision level fusion algorithms, some issues on hyperspectral remote sensing image classification based on decision level fusion are explored. In this three decision level fusion methods and four schemes for input data are used to hyperspectral remote sensing image classification

References
  1. Peg Shippert” Introduction to Hyperspectral Image Analysis” Earth Science Applications Specialist Research Systems, Inc
  2. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park
  3. B. Waske and J. A. Benediktsson, “Fusion of support vector machines for classification of multisensor data,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3858–3866, Dec. 2007
  4. J. A. Benediktsson and P. H. Swain, “Consensus theoretic classification methods,” IEEE Trans. Syst., Man, Cybern., vol. 22, pp. 688–704, July/Aug. 1992.
  5. J. A. Benediktsson, J. R. Sveinsson, and P. H. Swain, “Hybrid consensus theoretic classification,” IEEE Trans. Geosci. Remote Sensing, vol. 35,pp. 833–843, July 1997.
  6. Tzu-Chao Lin,” Improving D-S Evidence Theory for Data Fusion System,”
  7. M. Petrakos, J. A. Benediktsson, and I. Kanellopoulos, “The effect of classifier agreement on the accuracy of the combined classifier in decision level fusion,” IEEE Trans. Geosci.Remote Sensing, vol.39, no.11, pp.2539-2546, Nov 2001.
  8. Peijun Du, Wei Zhang, Shubi Zhang and Junshi Xia,’”Hyperspectral Remote Sensing Image Classification Based OnDecision Level Fusion,”Geoscience and RemoteSensing Symposium,2009 IEEE International,IGARSS 2009
Index Terms

Computer Science
Information Sciences

Keywords

Hyperspectral image classification Supervised classification Unsupervised Classification Fusion Decision Fusion