An automatic recognition and processing method for agglomeration area in aggregated area groups

doi:10.11947/j.AGCS.2019.20180056

Abstract

Abstract: Agglomeration operation is a core component of the automated generalization of aggregated area groups with adjacent and compact features. However, the traditional methods are limited to the general conceptual level and cannot support intelligent computer processing. So this paper proposed an automatic recognition and processing method for agglomeration areas in aggregated area groups. Firstly, the bridging area width index (WI), distribution pattern index (DPI), effective connection index (ECI) and overlap index (OI), which represent the typical characteristics of agglomeration area are summarized based on the Gestalt principle, then the agglomeration areas are identified automatically. Secondly, two key algorithms of agglomeration are optimized to achieve automatic processing, including external boundary outlines computation and agglomeration line extraction. Finally, the reliability and efficiency of the proposed method have been validated by using the actual data of topographic map in a typical area of Jiangsu province.

Key words: agglomeration area, Delaunay triangulation network, agglomeration, dilation and erosion transformations, skeleton line correction

CLC Number:

P208

LI Chengming, YIN Yong, WU Pengda, LIU Xiaoli. An automatic recognition and processing method for agglomeration area in aggregated area groups[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(3): 363-373.

References

[1] DELUCIA A A, BLACK R T. A comprehensive approach to automatic feature generalization[C]. Proceedings of the 13th Conference of the International Cartographic Conference. Morelia, Mexico:[s.n.], 1987(4):169-192.
[2] LI Zhilin. Algorithmic foundation of multi-scale spatial representation[M]. Bacon Raton:CRC Press, 2007.
[3] 王家耀, 李志林, 武芳. 数字地图综合进展[M]. 北京:科学出版社, 2011. WANG Jiayao, LI Zhilin, WU Fang. Advances in digital map generalization[M]. Beijing:Science Press, 2011.
[4] 艾廷华, 郭宝辰, 黄亚峰. 1:5万地图数据库的计算机综合缩编[J]. 武汉大学学报(信息科学版), 2005, 30(4):297-300. AI Tinghua, GUO Baochen, HUANG Yafeng. Construction of 1:50000 map database by computer generalization method[J]. Geomatics and Information Science of Wuhan University, 2005, 30(4):297-300.
[5] 蒙印, 艾廷华, 杨井源. 1:250000水系要素综合缩编技术方法[J]. 测绘与空间地理信息, 2014, 37(3):201-203. MENG Yin, AI Tinghua, YANG Jingyuan. A map generalization method for 1:250000 hydrographic feature[J]. Geomatics & Spatial Information Technology, 2014, 37(3):201-203.
[6] RUAS A. Multiple paradigms for automating map generalization:Geometry, topology, hierarchical partitioning and local triangulation[C]//American Congress on Surveying and Mapping, American Society for Photogrammetry and Remote Sensing.[S.l.]:ACSM1995:69-68.
[7] CHENG Pengfei, YAN Haowen, HAN Zhenhui. An algorithm for computing the minimum area bounding rectangle of an arbitrary polygon[J]. Journal of Engineering Graphics, 2008, 29(1):122-126.
[8] WARE J M, JONES C B, BUNDY G L. A triangulated spatial model for cartographic generalization of areal objects[C]//KRAAK M J, MOLENAAR M. Advance in GIS Research Ⅱ (the 7th Int. Symposium on Spatial Data Handling). London:Taylor & Francis, 1997:173-192.
[9] 艾廷华, 郭仁忠. 支持地图综合的面状目标约束Delaunay三角网剖分[J]. 武汉测绘科技大学学报, 2000, 25(1):35-41. AI Tinghua, GUO Renzhong. A constrained delaunay partitioning of areal objects to support map generalization[J]. Journal of Wuhan Technical University of Surveying and Mapping, 2000, 25(1):35-41.
[10] 祝国瑞. 地图学[M]. 武汉:武汉大学出版社, 2004. ZHU Guorui. Cartography[M]. Wuhan:Wuhan University Press, 2004.
[11] 黄亚锋, 艾廷华, 刘鹏程. 顾及Gestalt认知效应的线性岛屿模式识别[J]. 武汉大学学报(信息科学版), 2011, 36(6):717-720. HUANG Yafeng, AI Tinghua, LIU Pengcheng. Linear island alignment recognition based on gestalt principle[J]. Geomatics and Information Science of Wuhan University, 2011, 36(6):717-720.
[12] 王骁, 钱海忠, 何海威, 等. 利用空白区域骨架线网眼匹配多源面状居民地[J]. 测绘学报, 2015, 44(8):927-935. DOI:10.11947/j.AGCS.2015.20140462. WANG Xiao, QIAN Haizhong, HE Haiwei, et al. Matching multi-source areal habitations with skeleton line mesh of blank region[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(8):927-935. DOI:10.11947/j.AGCS.2015.20140462.
[13] 艾廷华, 郭仁忠. 支持地图综合的面状目标约束Delaunay三角网剖分[J]. 武汉测绘科技大学学报, 2000, 25(1):35-41. AI Tinghua, GUO Renzhong. A constrained delaunay partitioning of areal objects to support map generalization[J]. Journal of Wuhan Technical University of Surveying and Mapping, 2000, 25(1):35-41.
[14] ZOU Jujia, YAN Hong. Skeletonization of ribbon-like shapes based on regularity and singularity analyses[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2001, 31(3):401-407.
[15] MORRISON P, ZOU Jujia. Triangle refinement in a constrained delaunay triangulation skeleton[J]. Pattern Recognition, 2007, 40(10):2754-2765.
[16] 陈涛, 艾廷华. 多边形骨架线与形心自动搜寻算法研究[J]. 武汉大学学报(信息科学版), 2004, 29(5):443-446, 455. CHEN Tao, AI Tinghua. Automatic extraction of skeleton and center of area feature[J]. Geomatics and Information Science of Wuhan University, 2004, 29(5):443-446, 455.
[17] 王中辉, 闫浩文. 多边形主骨架线提取算法的设计与实现[J]. 地理与地理信息科学, 2011, 27(1):42-44, 48. WANG Zhonghui, YAN Haowen. Design and implementation of an algorithm for extracting the main skeleton lines of polygons[J]. Geography and Geo-Information Science, 2011, 27(1):42-44, 48.
[18] 刘远刚, 郭庆胜, 孙雅庚, 等. 地图目标群间骨架线提取的算法研究[J]. 武汉大学学报(信息科学版), 2015, 40(2):264-268. LIU Yuangang, GUO Qingsheng, SUN Yageng, et al. An algorithm for skeleton extraction between map objects[J]. Geomatics and Information Science of Wuhan University, 2015, 40(2):264-268.
[19] MITROPOULOS V, XYDIA A, NAKOS B, et al. The use of epsilon-convex area for attributing bends along a cartographic line[C]//Proceedings of the 12th International Cartographic Conference. la Corona, Spain:[s.n.], 2005.
[20] PARK S C, CHUNG Y C. Mitered offset for profile machining[J]. Computer-Aided Design, 2003, 35(5):501-505.
[21] YI I L, LEE Y S, SHIN H. Mitered offset of a mesh using QEM and vertex split[C]//Proceedings of the 2008 ACM Symposium on Solid and Physical Modeling. New York, NY:ACM, 2008:315-320.
[22] 唐常青, 吕宏伯, 黄铮, 等. 数学形态学方法及其应用[M]. 北京:科学出版社, 1990. TANG Changqing, LÜ Hongbo, HUANG Zheng, et al. Mathematical morphology method and its application[M]. Beijing:Science Press, 1990.
[23] SERNA A, MARCOTEGUI B. Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014(93):243-255.
[24] HAUNERT J H, SESTER M. Area collapse and road centerlines based on straight skeletons[J]. GeoInformatica, 2008, 12(2):169-191.
[25] 刘小凤, 吴艳兰, 胡海. 面状要素的多层次骨架线提取[J]. 测绘学报, 2013, 42(4):588-594. LIU Xiaofeng, WU Yanlan, HU Hai. A method of extracting multiscale skeletons for polygonal shapes[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4):588-594.
[26] MULLER J C. Fractal and automated line generalization[J]. The Cartographic Journal, 1987, 24(1):27-34.