Decision tree regression for soft classification of remote sensing data

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

Research output: Contribution to journalArticlepeer-review

403 Scopus citations


In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data. Their implementation as a per-pixel based classifier to produce hard or crisp classification has been reported in the literature. Remote sensing images, particularly at coarse spatial resolutions, are contaminated with mixed pixels that contain more than one class on the ground. The per-pixel approach may result in erroneous classification of images dominated by mixed pixels. Therefore, soft classification approaches that decompose the pixel into its class constituents in the form of class proportions have been advocated. In this paper, we employ a decision tree regression approach to determine class proportions within a pixel so as to produce soft classification from remote sensing data. Classification accuracy achieved by decision tree regression is compared with those achieved by the most widely used maximum likelihood classifier, implemented in the soft mode, and a supervised version of the fuzzy c-means classifier. Root Mean Square Error (RMSE) and fuzzy error matrix based measures have been used for accuracy assessment of soft classification.

Original languageEnglish (US)
Pages (from-to)322-336
Number of pages15
JournalRemote Sensing of Environment
Issue number3
StatePublished - Aug 15 2005


  • Classification accuracy
  • Decision tree regression
  • Non-parametric classification
  • Soft classification

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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