Accuracy assessment measures for object extraction from remote sensing images

Liping Cai, Wenzhong Shi, Zelang Miao, Ming Hao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm.

Original languageEnglish
Article number303
JournalRemote Sensing
Issue number2
Publication statusPublished - 1 Feb 2018


  • Accuracy assessment
  • Distance difference
  • Feature similarity
  • Object-based image analysis

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Accuracy assessment measures for object extraction from remote sensing images'. Together they form a unique fingerprint.

Cite this