Reduced-dimension clustering for vegetation segmentation

Brian L. Steward, Lei F. Tian, Dan Nettleton, Lie Tang

Research output: Contribution to journalArticlepeer-review


Segmentation of vegetation is a critical step in using machine vision for field automation tasks. A new method called reduced-dimensionclustering (RDC) was developed based on theoretical considerations about the color distribution of field images. RDC performed unsupervised classification of pixels infield images into vegetation and background classes. Bayes classifiers were then trained and used for vegetation segmentation. The performance of the classifiers trained using the RDC method was compared with that of other segmentation methods. The RDC method produced segmentation performance that was consistently high, with average segmentation success rates of 89.6% and 91.9% across both cloudy and sunny lighting conditions, respectively. Statistical analyses of segmentation performance coupled with three-dimensional visualization of classifier decision surfaces produced insight into why classifier performance varied across the methods. These results should lead to improvements in segmentation methods for field images acquired under variable lighting conditions.

Original languageEnglish (US)
Pages (from-to)609-616
Number of pages8
JournalTransactions of the American Society of Agricultural Engineers
Issue number2
StatePublished - Mar 2004


  • Cluster analysis
  • Machine vision
  • Segmentation
  • Vegetation detection

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)


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