Geometric Similarity Measurement Method for Micro Scene Generalization

Xiaomin Lu,Fei Yang,Haowen Yan,Zhonghui Wang

doi:10.3390/app12020628

Abstract

Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene’s geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little.

Highlights

Matching and analyzing geometric similarities play important roles in the field of GIS, and these tasks have garnered considerable research attention [1,2,3]
In the field of cartography, line groups and polygon groups often exist in the form of composite features, such as complex river sections that are composed of islands and river bbaannkkss,aasswweelllaassbblolocckkssththaattaarreeccoommppoosseeddooffroroaaddssaannddbbuuilidldininggss. .TTrraadditiitoionnaallssttuuddiieess mmoossttllyyddiivviiddeeddtthheeaabboovvee--mmeennttiioonneedd ccoommppoossiittee ffeeaattuurreess iinnttoo lliinneeggrroouuppssaannddppoolylyggoonn ggrroouuppss ffoorr separate rreesseeaarrcchh[[1155––1177]],ththeererebbyyspslpitltitintigngthtehoevoevraelrlaclol nconnecnteioctniobnetbweetwenetehne tehleemelemntes.nts
We examined the geometric similarity of micro scenes from the perspective of generalization

Summary

Introduction

Matching and analyzing geometric similarities play important roles in the field of GIS, and these tasks have garnered considerable research attention [1,2,3]. In the field of cartography, line groups and polygon groups often exist in the form of composite features, such as complex river sections that are composed of islands and river. In the field of cartography, line groups and polygon groups often exist in the form of composite features, such as complex river sections that are composed of islands and river bbaannkkss,,aasswweelllaassbblolocckkssththaattaarreeccoommppoosseeddooffroroaaddssaannddbbuuilidldininggss. 2. Methodology A micro scene contains line groups and polygon groups; shape transformatio A micro srceecntieocnotnratanisnfsolrimneatgiroonu, pans danpdospiotiloyngotrnangsrfoourpmsa; tsihoanpsehtoruanldsfboermcoantsioidne, rdeidreicn- the simi tion transformcaatlicounla, taionnd opfomsiticioron strcaennsefso(rFmigautiroen2)s.hIonutlhdisbsetucodnys,iwdeerecodnisntrtuhcetesdimailmareitthyod of eva calculation of minigcrtohescgeenoems (eFtirgicurseim2)i.laIrnittyhiosfsmtuidcryo, wsceecnoenssbtrauscetdedona hmyebtrhioddfeoafteuvraeludaetsicnrgiption, a d the geometrictsioimn iVlaorritoynooif dmiaicgrroamsc,enanesdbaapseodsiotinonhygbrarpidh.feSahtaupree dsiemscilraiprittiyonw, aasdciarleccutiloatned by com Voronoi diagrianmg,tahnedInaclpuodseitdioAnnggrlaepChh. aSihnaapnedsitmheilaHraituysdwoarsffcadlicsutalantceed; tbhye cdoimrebctiinoinnagl Vorono the Included AgrnagmlewCahsauinseadndtothcaelcHualautsedtohreffddirisetcatniocne;stihmeildairrietyc;titohneaDl Veolaruonnaoyi dtriiaagnrgaumlation net was used to cwalcaus lcaotnestthrue cdteirdecttoiocnalcsuimlaitleartihtye;ptohseitDioenlasuimnailyartirtiya.ngulation network was constructed to calculate the position similarity.

Shape Feature Extraction by Hausdorff Distance and Included Angle Chain

Double Lines Shape Similarity Calculation

Direction Voronoi Graph Model

Micro Scenes Position Similarity

Geometric Similarity Measurements of Micro Scenes

Conclusions