A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships. Issue 5 (4th May 2017)

• Record Type: Journal Article
• Authors: Kim, Ick-Hoi; Feng, Chen-Chieh; Wang, Yi-Chen
• Is Part Of: International journal of geographical information science. Volume 31:Issue 5(2017)
• Page Start: 1042
• Page End: 1060
• Publication Date: 2017-05-04
• Subjects: Feature matching -- data conflation -- Hausdorff distance -- cosine similarity -- historical data; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2016.1267736
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Linear feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified linear feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple linear feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for linear feature matching.