The division of the internal structure and external space of geographical entities is the premise of the analysis of topological relations and directional relations. Currently, most division methods are crisp, which does not conform to human cognitive habits. Occasionally, users want to integrate their meaning by vague natural language when they understand spatial scenarios; however, natural language suffers from uncertainty due to the effects of individual characteristics and the context of environmental factors. To handle uncertainties in spatial conceptions of regional features, the semantic spatial partitioning model for regions based on computing with words is proposed. The structure of a region is divided into several parts using fuzzy logic according to people’s cognitive habits, and then, a detailed direction model of regions is proposed. The proposed method is applied to understand a remote sensing dataset, and the results show that the proposed method can enrich the understanding of images while conforming to human cognitive habits.
DiloA., Representation of and reasoning with vagueness in spatial information: A system for handling vague objects, PhD thesis, ITC, Enschede, 2006.
2.
SharifA.R., EgenhoferM.J. and MarkD., Natural-language spatial relations between linear and areal objects: The topology and metric of English-language terms, Int J Geogr Inform Sci12(3) (1998), 215–246.
3.
LiC.C., RodriguezR.M., MartinezL., et al., Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions, Knowl Based Syst145 (2018), 156–165.
4.
LiC.C., DongY.C., HerreraF., et al., Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching, Inform Fusion33 (2017), 29–40.
5.
WuD., A reconstruction decoder for computing with words, Inform Sci255 (2014), 1–15.
6.
BustinceH., FernandezJ., HagrasJ., et al., Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Toward a wider view on their relationship, IEEE Trans Fuzzy Syst23(5) (2015), 1876–1882.
7.
GuoJ.F., MaoJ., CuiT., et al., A multi-scale fuzzy spatial analysis framework for large data based on IT2 FS, Int J Uncertainty Fuzziness Knowl Based Syst23(1) (2015), 73–104.
8.
GuoJ.F. and CuiT.J., Discussion on type-I fuzzy boundary and research on boundary definition of high order fuzzy region, TELKOMNIKA Indonesian J Electr Eng10(6) (2012), 1207–1213.
9.
GuoJ.F. and CuiW.H., 8-directions fuzzy asymmetric model and analysis of its uncertainty conduced by reference point’s location error, J Remote Sens14 (2010), 879–892.
10.
GuoJ.F. and CuiW.H., Modeling and metrics of higher order fuzzy geographical phenomena, Acta Geodaet Cartogr Sin41(1) (2012), 139–146.
11.
GuoJ.F. and ShaoX.D., A fine fuzzy spatial partitioning model for line objects based on computing with words and application in natural language spatial query, J Intell Fuzzy Syst32 (2017), 2017–2032.
12.
MendelJ.M., WuD., Perceptual computing: Aiding people in making subjective judgments, IEEE Press Series on Computational Intelligence, IEEE Press, 2010.
13.
MendelJ.M., Uncertain Rule-based Fuzzy Systems Introduction and New Directions, 2nd, Springer, 2017.
14.
MendelJ.M. and JohnR., Type-2 fuzzy sets made simple, IEEE Trans Fuzzy Syst10 (2002), 117–127.
15.
XiaoJ., ZhangY. and FuC., Comparison between methods of interval number ranking based on possibility, J Tianjin Univ44 (2011), 705–711.
16.
XuJ., Natural—language representation and query of liner geographic objects in GIS, PhD dissertation, Department of Geography, State University of New York at Buffalo, 2005.
17.
BohnemeyerJ., DonelsonK.T., MooreR.E., et al., The contact diffusion of linguistic practices: Reference frames in Mesoamerica, Lang Dyn Change5(2) (2015), 169–201.
18.
GuoJ.F. and HuoH.Y., An enhanced IT2FCM* algorithm integrating spectral indices and spatial information for multi-spectral remote sensing image clustering, Remote Sens9 (2017), 960.
19.
GuoJ.F., CuiW.H., LiW.X., et al., A direction relation description model based on IT2 FLS, Geom Inform Sci Wuhan Univ35 (2010), 1365–1368.
20.
WangJ.H. and HaoJ., A new version of 2-tuple fuzzy linguistic representation model for computing with words, IEEE Trans Fuzzy Syst14(3) (2006), 435–445.
21.
MendelJ.M., Fuzzy sets for words: A new beginning, 12th IEEE International Conference on Fuzzy systems (FUZZ’03), Institute of Electrical and Electronics Engineers, St. Louis, (2003), 37–42.
22.
NedasK.A., EgenhoferM.J. and WilmsenD., Metric details of topological line-line relations, Int J Geogr Inform Sci21(1) (2007), 21–48.
23.
LiuK. and ShiW., Quantitative fuzzy topological relations of spatial objects by induced fuzzy topology, Int J Appl Earth Observ Geoinform11(1) (2009), 38–45.
24.
ChangK.T., Introduction to Geographic Information Systems (3e), McGraw-Hill, 2009.
25.
BejaouiL., PinetF., BedardY., et al., Qualified topological relations between spatial objects with possible vague shape, Int J Geogr Inform Sci23(7) (2009), 877–921.
ZadehL.A., From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions, IEEE Trans Circuit Syst I45 (1999), 105–109.
28.
ZadehL.A., Fuzzy logic=computing with words, IEEE Trans Fuzzy Syst4 (1996), 103–111.
29.
ZadehL.A., The concept of a linguistic variable and its application to approximate reasoning, Inf Sci8 (1975), 199–249.
30.
DengM., XuR., LinZ., et al., A spatial-query-driven transformation between metric spatial relations and natural-language spatial relations: Taking regions as example, Acta Geodaetica Cartogr Sin38(6) (2009), 527–531.
31.
EgenhoferM.J. and ShariffA.R.B.M., Metric details for natural-language spatial relations, ACM Trans Inform Syst16(4) (1998), 295–321.
32.
EgenhoferM.J. and FranzosaR.D., Point-set topological spatial relations, Int J Geogr Inform Syst5 (1991), 161–174.
33.
DubeM.P., Algebraic refinements of direction relations through topological augmentation, Doctoral Dissertation, Spatial Information Science and Engineering, The University of Maine, 2016.
34.
SchneiderM., ChenT., ViswanathanG., et al., Cardinal directions between complex regions, ACM Trans Database Syst37(2) (2012), 1–40.
35.
SchneiderM., Uncertainty management for spatial data in databases: Fuzzy spatial data types, 6th International Symposium on Advances in Spatial Databases, LNCS 1651, Springer-Verlag, Berlin, 330–351. 1999.
36.
KarnikN.N. and MendelJ.M., Centroid of a type-2 fuzzy set, Inform Sci132 (2001), 195–220.
37.
AdamN.R., ShafiqaB. and StaffinR., Spatial computing and social media in the context of disaster management, IEEE Intell Syst27(6) (2012), 90–96.
38.
FisherP., ChengT. and WoodJ., Higher order vagueness in geographical information: Empirical geographical population of type fuzzy sets, Geoinformatica11 (2007), 311–330.
39.
FisherP.F., Remote sensing of land cover classes as type 2 fuzzy sets, Remote Sens Environ114 (2010), 309–321. doi:10.1016/j.rse.2009.09.004.
40.
GoyalR., Similarity assessment for cardinal directions between extended spatial objects, Doctoral Dissertation, University of Maine, Maine, 2000.
41.
RodríguezR.M., LabellaÁ. and MartínezL., An overview on fuzzy modelling of complex linguistic preferences in decision making, Int J Comput Intell Syst9(Supp 1) (2016), 81–94.
42.
DuS., WangQ. and YangY., A qualitative description model of detailed direction relations, J Image Graph9(12) (2004), 1496–1503.
43.
DuS., WangX., FengC., et al., Classifying natural-language spatial relation terms with random forest algorithm, Int J Geogr Inform Sci31(3) (2017), 542–568.
44.
SchockaertS., Reasoning about fuzzy temporal and spatial information from the Web, Doctoral Dissertation, Ghent University, Ghent, Belgium, 2008.
45.
FrankU., Qualitative spatial reasoning about cardinal directions. In: MarkD., WhiteD., eds. Proceedings of Austrian Conference on Artificial Intelligence (1991), pp. 157–167.
46.
PopovichV., et al., eds., Information Fusion and Geographic Information Systems (IF&GIS 2013), Lecture Notes in Geoinformation and Cartography, Springer-Verlag, Berlin, Heidelberg, 2014.
47.
TangX., Spatial Object Modeling in Fuzzy Topological Spaces with Applications to Land Cover Change, the Netherlands, 2004.
48.
YaoX. and ThillJ., How far is too far? A statistical approach to context-contingent proximity modeling, Trans GIS9 (2005), 157–178.
49.
ZhangX., ZhangC. and DuC., Semantic relation between spatial relation terms and feature types of geographical entities, Geomatics Inform Sci Wuhan Univ37(11) (2012), 1266–1270.
50.
DongY.C., LiC.C. and HerreraF., Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its uses to deal with hesitant unbalanced linguistic information, Inform Sci367-368 (2016), 259–278.
51.
DongY.C., ZhangG.Q., HongW.C., et al., Linguistic computational model based on 2-tuples and intervals, IEEE Trans Fuzzy Syst21 (2013), 1006–1018.
