Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16314
Title: Identification of multi-scale corresponding object-set pairs between two polygon datasets with hierarchical co-clustering
Authors: Huh, Y
Kim, J
Lee, J
Yu, K
Shi, W 
Keywords: Composite NDVI image
Forest inventory map
Geographic object-based image analysis
Hierarchical co-clustering
Laplacian-graph embedding
Multi-scale object-set matching
Issue Date: 2014
Source: ISPRS Journal of Photogrammetry and Remote Sensing, 2014, v. 88, p. 60-68 How to cite?
Journal: ISPRS Journal of Photogrammetry and Remote Sensing 
Abstract: 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.
URI: http://hdl.handle.net/10397/16314
ISSN: 0924-2716
DOI: 10.1016/j.isprsjprs.2013.11.017
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