Focal-test-based spatial decision tree learning

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

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural resource management. However, the problem is challenging due to the fact that learning samples show spatial autocorrelation in class labels, instead of being independently identically distributed. Related work relies on local tests (i.e., testing feature information of a location) and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. In contrast, we recently proposed a focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information. Preliminary results showed that FTSDT reduces classification errors and salt-and-pepper noise. This paper extends our recent work by introducing a new focal test approach with adaptive neighborhoods that avoids over-smoothing in wedge-shaped areas. We also conduct computational refinement on the FTSDT training algorithm by reusing focal values across candidate thresholds. Theoretical analysis shows that the refined training algorithm is correct and more scalable. Experiment results on real world data sets show that new FTSDT with adaptive neighborhoods improves classification accuracy, and that our computational refinement significantly reduces training time.

Original languageEnglish (US)
Article number6963450
Pages (from-to)1547-1559
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number6
DOIs
StatePublished - Jun 1 2015

Keywords

  • focal-test-based spatial decision tree
  • land cover classification
  • spatial autocorrelation
  • spatial data mining

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