Segmentation quality evaluation using region-based precision and recall measures for remote sensing images

Xueliang Zhang, Xuezhi Feng, Pengfeng Xiao, Guangjun He, Liujun Zhu

Research output: Contribution to journalArticleResearchpeer-review

68 Citations (Scopus)

Abstract

Segmentation of remote sensing images is a critical step in geographic object-based image analysis. Evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and optimize their parameters. In this study, we propose region-based precision and recall measures and use them to compare two image partitions for the purpose of evaluating segmentation quality. The two measures are calculated based on region overlapping and presented as a point or a curve in a precision-recall space, which can indicate segmentation quality in both geometric and arithmetic respects. Furthermore, the precision and recall measures are combined by using four different methods. We examine and compare the effectiveness of the combined indicators through geometric illustration, in an effort to reveal segmentation quality clearly and capture the trade-off between the two measures. In the experiments, we adopted the multiresolution segmentation (MRS) method for evaluation. The proposed measures are compared with four existing discrepancy measures to further confirm their capabilities. Finally, we suggest using a combination of the region-based precision-recall curve and the F-measure for supervised segmentation evaluation.

Original languageEnglish
Pages (from-to)73-84
Number of pages12
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume102
DOIs
Publication statusPublished - Apr 2015
Externally publishedYes

Keywords

  • Discrepancy measure
  • Geographic object-based image analysis
  • High-spatial resolution remote sensing
  • Image segmentation
  • Precision and recall
  • Segmentation evaluation

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