Vectorization of classified remote sensing raster data to establish topological relations among polygons.
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| Title: | Vectorization of classified remote sensing raster data to establish topological relations among polygons. |
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| Authors: | Xu, Bin xubin20080108@163.com, Chen, Jianping 3s@cugb.edu.cn, Yu, Pingping ypp0228@163.com |
| Source: | Earth Science Informatics. Mar2017, Vol. 10 Issue 1, p99-113. 15p. |
| Subject Terms: | *Vector graphics, *Bit-mapped graphics, *Geographic information systems, *Remote sensing, *Data integration |
| Abstract: | Vector and raster are two types of spatial data structures used in a geographic information system (GIS). With the development of GIS and remote sensing (RS) technologies, how to rapidly convert raster to vector data and establish topological relations among vectorized polygons is becoming a bottleneck in data integration between GIS and RS. Based on the previous work, an improved vectorization method is proposed to vectorize classified RS raster data quickly and automatically establish topological relations. In accordance with the connection information of arcs and nodes and both-sides polygonal attributes of arcs, the next arc can be searched directly by attribute matching when constructing polygons, thereby improving search efficiency. Moreover, our method addressed the problems of self-intersecting polygons, shared-boundary, and multi-nested islands and gave corresponding solutions, which can establish the topological relations of an entire image quickly. Two experiments, one for comparison between before and after vectorization of two different classified RS raster maps, and the other for comparison with several methods, are carried out to test the accuracy and efficiency of our method. Results show that the method solves the self-intersecting polygons, shared-boundary, and multi-nested islands problems. In addition, its vectorization speed is more than double that of commercial software ArcGIS, and the advantage of our method becomes more obvious as the number of polygons increases. Thus, our method can vectorize large and complex classified RS raster data with sufficient efficiency for practical use and establish topological relations among vectorized polygons. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| Abstract: | Vector and raster are two types of spatial data structures used in a geographic information system (GIS). With the development of GIS and remote sensing (RS) technologies, how to rapidly convert raster to vector data and establish topological relations among vectorized polygons is becoming a bottleneck in data integration between GIS and RS. Based on the previous work, an improved vectorization method is proposed to vectorize classified RS raster data quickly and automatically establish topological relations. In accordance with the connection information of arcs and nodes and both-sides polygonal attributes of arcs, the next arc can be searched directly by attribute matching when constructing polygons, thereby improving search efficiency. Moreover, our method addressed the problems of self-intersecting polygons, shared-boundary, and multi-nested islands and gave corresponding solutions, which can establish the topological relations of an entire image quickly. Two experiments, one for comparison between before and after vectorization of two different classified RS raster maps, and the other for comparison with several methods, are carried out to test the accuracy and efficiency of our method. Results show that the method solves the self-intersecting polygons, shared-boundary, and multi-nested islands problems. In addition, its vectorization speed is more than double that of commercial software ArcGIS, and the advantage of our method becomes more obvious as the number of polygons increases. Thus, our method can vectorize large and complex classified RS raster data with sufficient efficiency for practical use and establish topological relations among vectorized polygons. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 18650473 |
| DOI: | 10.1007/s12145-016-0273-3 |
