Fusion of support vector machines for classifying SAR and multispectral imagery from agricultural areas

Björn Waske*, Gunter Menz, Jón Atli Benediktsson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

A concept for classifying multisensor data sets, consisting of multispectral and SAR imagery is introduced. Each data source is separately classified by a support vector machine (SVM). In a decision fusion the outputs of the preliminary SVMs are used to determine the final class memberships. This fusion is performed by another SVM as well as two common voting schemes. The results are compared with well-known parametric and nonparametric classifier methods. The proposed SVM-based fusion approach outperforms all other concepts and significantly improves the results of a single SVM that is trained on the whole multisensor data set.

Original languageEnglish
Title of host publication2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Pages4842-4845
Number of pages4
DOIs
Publication statusPublished - 2007
Event2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain
Duration: 23 Jun 200728 Jun 2007

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Country/TerritorySpain
CityBarcelona
Period23/06/0728/06/07

Other keywords

  • Classification
  • Decision fusion
  • Mulitsensor
  • Multispectral
  • SAR
  • Support vector machines

Fingerprint

Dive into the research topics of 'Fusion of support vector machines for classifying SAR and multispectral imagery from agricultural areas'. Together they form a unique fingerprint.

Cite this