TY - JOUR
T1 - An optimisation model for linear feature matching in geographical data conflation
AU - Li, Linna
AU - Goodchild, Michael F.
N1 - Funding Information:
This research was supported by the US Department of Transportation (USDOT), the National Geospatial-Intelligence Agency through the NGA University Research Initiative Program (NGA-NURI grant no. HM1582-10-1-0007) and the Army Research Office (ARO).
PY - 2011/12
Y1 - 2011/12
N2 - Issues of heterogeneity and incompatibility in geospatial data become increasingly important as data sources become more abundant. Scientific research and decision-making usually require geospatial data from a variety of sources, since it is not realistic to collect all data directly; therefore, it is important to effectively utilise data created by various agencies using different methodologies under different circumstances. The term conflation here refers to the problem of combining incompatible geospatial data. One crucial component in conflation is feature matching, which is a prerequisite for the subsequent steps such as feature transformation. Although previous research has provided different methods of feature matching for specific applications, most of them have relied on a greedy strategy to execute the matching process. This article develops a new optimisation model to improve linear feature matching in situations with one-to-one, one-to-many and one-to-none correspondences by extending the optimised feature matching method proposed by Li and Goodchild (Automatically and accurately matching objects in geospatial datasets. In: Proceedings of theory, data handling and modelling in geospatial information science. Hong Kong, 26-28 May, 2010). Considering all possible matched pairs simultaneously, this new model achieves a high percentage of correctly matched features by maximising the total similarity between all matched pairs. When autocorrelated distortions exist in the datasets, an affine transformation can be integrated into the feature matching to improve the matching results. In addition, this study takes advantage of the asymmetry of a dissimilarity metric - directed Hausdorff distance - to address one-to-many correspondences.
AB - Issues of heterogeneity and incompatibility in geospatial data become increasingly important as data sources become more abundant. Scientific research and decision-making usually require geospatial data from a variety of sources, since it is not realistic to collect all data directly; therefore, it is important to effectively utilise data created by various agencies using different methodologies under different circumstances. The term conflation here refers to the problem of combining incompatible geospatial data. One crucial component in conflation is feature matching, which is a prerequisite for the subsequent steps such as feature transformation. Although previous research has provided different methods of feature matching for specific applications, most of them have relied on a greedy strategy to execute the matching process. This article develops a new optimisation model to improve linear feature matching in situations with one-to-one, one-to-many and one-to-none correspondences by extending the optimised feature matching method proposed by Li and Goodchild (Automatically and accurately matching objects in geospatial datasets. In: Proceedings of theory, data handling and modelling in geospatial information science. Hong Kong, 26-28 May, 2010). Considering all possible matched pairs simultaneously, this new model achieves a high percentage of correctly matched features by maximising the total similarity between all matched pairs. When autocorrelated distortions exist in the datasets, an affine transformation can be integrated into the feature matching to improve the matching results. In addition, this study takes advantage of the asymmetry of a dissimilarity metric - directed Hausdorff distance - to address one-to-many correspondences.
KW - Affine transformation
KW - Conflation
KW - Directed hausdorff distance
KW - Feature matching
KW - Optimisation
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U2 - 10.1080/19479832.2011.577458
DO - 10.1080/19479832.2011.577458
M3 - Article
AN - SCOPUS:84859384255
SN - 1947-9832
VL - 2
SP - 309
EP - 328
JO - International Journal of Image and Data Fusion
JF - International Journal of Image and Data Fusion
IS - 4
ER -