Identification of multi-scale corresponding object-set pairs between two polygon datasets with hierarchical co-clustering

Yong Huh, Jiyoung Kim, Jeabin Lee, Kiyun Yu, Wen Zhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)


In this paper, we propose a means of finding multi-scale corresponding object-set pairs between two polygon datasets by means of hierarchical co-clustering. This method converts the intersection-ratio-based similarities of two objects from two datasets, one from each dataset, into the objects' proximity in a geometric space using a Laplacian-graph embedding technique. In this space, the method finds hierarchical object clusters by means of agglomerative hierarchical clustering and separates each cluster into object-set pairs according to the datasets to which the objects belong. These pairs are evaluated with a matching criterion to find geometrically corresponding object-set pairs. We applied the proposed method to the segmentation result of a composite image with 6 NDVI images and a forest inventory map. Regardless of the different origins of the datasets, the proposed method can find geometrically corresponding object-set pairs which represent hierarchical distinctive forest areas.
Original languageEnglish
Pages (from-to)60-68
Number of pages9
JournalISPRS Journal of Photogrammetry and Remote Sensing
Publication statusPublished - 1 Jan 2014


  • Composite NDVI image
  • Forest inventory map
  • Geographic object-based image analysis
  • Hierarchical co-clustering
  • Laplacian-graph embedding
  • Multi-scale object-set matching

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences

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