A hexagon-based method for polygon generalization using morphological operators. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Wang, Lu; Ai, Tinghua; Burghardt, Dirk; Shen, Yilang; Yang, Min
• Is Part Of: International journal of geographical information science. Volume 37:Issue 1(2023)
• Page Start: 88
• Page End: 117
• Publication Date: 2023-01-02
• Subjects: Polygon generalization -- hexagonal grids -- mathematical morphology -- raster data; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2108036
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins.