Fusion of multisource data sets from agricultural areas for improved land cover classification

Björn Waske, Jón Atli Benediktsson, Johannes R. Sveinsson

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

Abstract

An approach for spectral-spatial classification of multisource remote sensing data from agricultural areas is addressed. Mathematical morphology is used to derive the spatial information from the data sets. The different data sources (i.e., SAR and multispectral) are classified by support vector machines (SVM). Afterwards, the SVM outputs are transferred to probability measurements. These probability values are combined by different fusion strategies, to derive the final classification result. Comparing the results based on mathematical morphology the total accuracy increased by 6% compared to the pure-pixel classification results. Moreover the transfer of the SVM outputs into probability values and the subsequent fusion further increases the classification accuracy, resulting in an accuracy of 78.5%.

Original languageEnglish
Title of host publication2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
PagesIV952-IV955
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa
Duration: 12 Jul 200917 Jul 2009

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume4

Conference

Conference2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/TerritorySouth Africa
CityCape Town
Period12/07/0917/07/09

Other keywords

  • Data fusion
  • Land cover classification
  • Mathematical morphology
  • Multispectral
  • SAR

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