Crop classification using multiconfiguration C-band SAR data

F. Del Frate, G. Schiavon, D. Solimini, M. Borgeaud, D.H. Hoekman, M.A.M. Vissers

Research output: Contribution to journalArticleAcademicpeer-review

60 Citations (Scopus)


This paper reports on an investigation aimed at evaluating the performance of a neural-network based crop classification technique, which makes use of backscattering coefficients measured in different C-band synthetic aperture radar (SAR) configurations (multipolarization/multitemporal). To this end, C-band AirSAR and European Remote Sensing Satellite (ERS) data collected on the Flevoland site, extracted from the European RAdar-Optical Research Assemblage (ERA-ORA) library, have been used. The results obtained in classifying seven types of crops are discussed on the basis of the computed confusion matrices. The effect of increasing the number of polarizations and/or measurements dates are discussed and a scheme of interyear dynamic classification of five crop types is considered.
Original languageEnglish
Pages (from-to)1611-1619
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number7
Publication statusPublished - 2003


  • multifrequency polarimetric sar
  • learning neural-network
  • agricultural crops
  • capability
  • signatures
  • biomass
  • imagery
  • model

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