期刊名称：ISPRS International Journal of Geo-Information
电子版ISSN：2220-9964
出版年度：2021
卷号：10
期号：2
页码：75
DOI：10.3390/ijgi10020075
语种：English
出版社：MDPI AG
摘要：Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%.
