Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization
We used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/746094 |
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| _version_ | 1832566654060462080 |
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| author | Jun Yang Yuechen Li Jianchao Xi Chuang Li Fuding Xie |
| author_facet | Jun Yang Yuechen Li Jianchao Xi Chuang Li Fuding Xie |
| author_sort | Jun Yang |
| collection | DOAJ |
| description | We used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the two evaluation elements (namely, semantic consistency and semantic completeness) from the semantic perspective to conduct the contrast evaluation study on the generalization results from the two levels, respectively, namely, land type and map. The study results show that: (1) before and after the generalization, it is easier for the vector data to guarantee the area balance of the patch; the raster data’s aggregation of the small patch is more obvious. (2) Analyzing from the scale of the land type, most of the land use types of the two kinds of generalization result’s semantic consistency is above 0.6; the semantic completeness of all types of land use in raster data is relatively low. (3) Analyzing from the scale of map, the semantic consistency of the generalization results for the two kinds of data is close to 1, while, in the aspect of semantic completeness, the land type deletion situation of the raster data generalization result is more serious. |
| format | Article |
| id | doaj-art-f4bd971851db40ce9c58e7e9532fb447 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-f4bd971851db40ce9c58e7e9532fb4472025-02-03T01:03:34ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/746094746094Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch GeneralizationJun Yang0Yuechen Li1Jianchao Xi2Chuang Li3Fuding Xie4Liaoning Normal University, Liaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaLiaoning Normal University, Liaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaInstitute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaLiaoning Normal University, Liaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaLiaoning Normal University, Liaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaWe used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the two evaluation elements (namely, semantic consistency and semantic completeness) from the semantic perspective to conduct the contrast evaluation study on the generalization results from the two levels, respectively, namely, land type and map. The study results show that: (1) before and after the generalization, it is easier for the vector data to guarantee the area balance of the patch; the raster data’s aggregation of the small patch is more obvious. (2) Analyzing from the scale of the land type, most of the land use types of the two kinds of generalization result’s semantic consistency is above 0.6; the semantic completeness of all types of land use in raster data is relatively low. (3) Analyzing from the scale of map, the semantic consistency of the generalization results for the two kinds of data is close to 1, while, in the aspect of semantic completeness, the land type deletion situation of the raster data generalization result is more serious.http://dx.doi.org/10.1155/2014/746094 |
| spellingShingle | Jun Yang Yuechen Li Jianchao Xi Chuang Li Fuding Xie Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization Abstract and Applied Analysis |
| title | Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization |
| title_full | Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization |
| title_fullStr | Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization |
| title_full_unstemmed | Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization |
| title_short | Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization |
| title_sort | study on semantic contrast evaluation based on vector and raster data patch generalization |
| url | http://dx.doi.org/10.1155/2014/746094 |
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