Focal-test-based spatial decision tree learning: A summary of results

Zhe Jiang, Shashi Shekhar, Xun Zhou, Joseph Knight, Jennifer Corcoran

Research output: Contribution to journalConference articlepeer-review

19 Scopus citations


Given a raster spatial framework, as well as training and test sets, the spatial decision tree learning (SDTL) problem aims to minimize classification errors as well as salt-and-pepper noise. The SDTL problem is important due to many societal applications such as land cover classification in remote sensing. However, the SDTL problem is challenging due to the spatial autocorrelation of class labels, and the potentially exponential number of candidate trees. Related work is limited due to the use of local-test-based decision nodes, which can not adequately model spatial autocorrelation during test phase, leading to high salt-and-pepper noise. In contrast, we propose a focal-test-based spatial decision tree (FTSDT) model, where the tree traversal direction for a location is based on not only local but also focal (i.e., neighborhood) properties of the location. Experimental results on real world remote sensing datasets show that the proposed approach reduces salt-and-pepper noise and improves classification accuracy.

Original languageEnglish (US)
Article number6729516
Pages (from-to)320-329
Number of pages10
JournalProceedings - IEEE International Conference on Data Mining, ICDM
StatePublished - 2013
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013


  • focal test
  • spatial autocorrelation
  • spatial data mining
  • spatial decision tree


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