Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN
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 a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/2/75 |
| _version_ | 1827589324817825792 |
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| author | Daoye Zhu Chengqi Cheng Weixin Zhai Yihang Li Shizhong Li Bo Chen |
| author_facet | Daoye Zhu Chengqi Cheng Weixin Zhai Yihang Li Shizhong Li Bo Chen |
| author_sort | Daoye Zhu |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-09T00:54:03Z |
| format | Article |
| id | doaj.art-ed58d9130e3e4474bb893feaacbf0f12 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T00:54:03Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-ed58d9130e3e4474bb893feaacbf0f122023-12-11T17:00:46ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-02-011027510.3390/ijgi10020075Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNNDaoye Zhu0Chengqi Cheng1Weixin Zhai2Yihang Li3Shizhong Li4Bo Chen5Center for Data Science, Peking University, Beijing 100871, ChinaCenter for Data Science, Peking University, Beijing 100871, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaTH Center of China, Beijing 100094, ChinaInstitute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaSpatial 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%.https://www.mdpi.com/2220-9964/10/2/75multi-scalespatial polygonal objectmatchgranularity factorBPNN |
| spellingShingle | Daoye Zhu Chengqi Cheng Weixin Zhai Yihang Li Shizhong Li Bo Chen Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN ISPRS International Journal of Geo-Information multi-scale spatial polygonal object match granularity factor BPNN |
| title | Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN |
| title_full | Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN |
| title_fullStr | Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN |
| title_full_unstemmed | Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN |
| title_short | Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN |
| title_sort | multiscale spatial polygonal object granularity factor matching method based on bpnn |
| topic | multi-scale spatial polygonal object match granularity factor BPNN |
| url | https://www.mdpi.com/2220-9964/10/2/75 |
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