Unsupervised Quantification of Under-and Over-Segmentation for Object-Based Remote Sensing Image Analysis

Andres Troya-Galvis, Pierre Gancarski, Nicolas Passat, Laure Berti-Equille

Research output: Contribution to journalArticle

24 Citations (Scopus)


Object-based image analysis (OBIA) has been widely adopted as a common paradigm to deal with very high-resolution remote sensing images. Nevertheless, OBIA methods strongly depend on the results of image segmentation. Many segmentation quality metrics have been proposed. Supervised metrics give accurate quality estimation but require a ground-truth segmentation as reference. Unsupervised metrics only make use of intrinsic image and segment properties; yet most of them strongly depend on the application and do not deal well with the variability of objects in remote sensing images. Furthermore, the few metrics developed in a remote sensing context mainly focus on global evaluation. In this paper, we propose a novel unsupervised metric, which evaluates local quality (per segment) by analyzing segment neighborhood, thus quantifying under-and over-segmentation given a certain homogeneity criterion. Additionally, we propose two variants of this metric, for estimating global quality of remote sensing image segmentation by the aggregation of local quality scores. Finally, we analyze the behavior of the proposed metrics and validate their applicability for finding segmentation results having good tradeoff between both kinds of errors.

Original languageEnglish
Article number7112093
Pages (from-to)1936-1945
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number5
Publication statusPublished - 1 May 2015


  • image region analysis
  • Image segmentation
  • object oriented methods
  • quality control

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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