Multisource fusion for land cover classification using support vector machines

Pakorn Watanachaturaporn, Pramod K. Varshney, Manoj K. Arora

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Remote sensing data have proven to be an attractive source for extracting accurate land cover information. For a given application, information from an individual sensor may be incomplete, inconsistent, and imprecise. Additional data sources may assist in achieving a higher degree of accuracy. Recently, support vector machines (SVM), a non-parametric algorithm, has been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, the use of the SVM algorithm for multisource classification has been investigated. An IRS-1C LISS III image along with NDVI and DEM data layers in the Himalayan region were fused for classification. The results illustrate a significant improvement in accuracy of classification on incorporation of ancillary data over the classification performed solely on the basis of remote sensing data.

Original languageEnglish (US)
Title of host publication2005 7th International Conference on Information Fusion, FUSION
PublisherIEEE Computer Society
Pages614-621
Number of pages8
ISBN (Print)0780392868, 9780780392861
DOIs
StatePublished - Jan 1 2005
Event2005 8th International Conference on Information Fusion, FUSION - Philadelphia, PA, United States
Duration: Jul 25 2005Jul 28 2005

Publication series

Name2005 7th International Conference on Information Fusion, FUSION
Volume1

Conference

Conference2005 8th International Conference on Information Fusion, FUSION
CountryUnited States
CityPhiladelphia, PA
Period7/25/057/28/05

Keywords

  • Information fusion
  • Land cover
  • Multisource classification
  • Remote sensing
  • Support vector machines

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Watanachaturaporn, P., Varshney, P. K., & Arora, M. K. (2005). Multisource fusion for land cover classification using support vector machines. In 2005 7th International Conference on Information Fusion, FUSION (pp. 614-621). [1591911] (2005 7th International Conference on Information Fusion, FUSION; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICIF.2005.1591911