M:N object matching between image and map object data sets by means of latent semantic analysis

Abstract

In this article, we propose a method to find corresponding object-set pairs between image and map polygon object data sets by means of latent semantic analysis. Latent semantic analysis assigns each polygon object of both data sets to feature vectors in a continuous geometric space in which the similarities between the vectors are proportional to the priorities to constitute a corresponding object-set pair. Thus, object clusters can be obtained by applying an agglomerative hierarchical clustering to the feature vectors. These object clusters are separated into object-set pairs according to the data sets to which the objects belong and are evaluated with a geometric matching criterion to find corresponding object-set pairs. We applied the proposed method to the segmentation result of a composite image with six normalized difference vegetation index (NDVI) images and a forest inventory map. The proposed method was compared to a graph-embedding-based method. The results showed that the proposed method found more corresponding object-set pairs with a similar accuracy in terms of shape similarities and shared information of found pairs.

Acknowledgement

This research was supported by the Ministry of Land, Infrastructure, and Transport, Korea, under the Urban Planning & Architecture (UPA) research support programme, supervised by the Korea Agency for Infrastructure Technology Advancement (KAIA) (13 Urban Planning & Architecture 02).