The rough expression for topological relation of GIS entity based on binary relation

WeiHua Liao

doi:10.1117/12.866308

20 August 2010 The rough expression for topological relation of GIS entity based on binary relation

WeiHua Liao

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78203E (2010) https://doi.org/10.1117/12.866308
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

Abstract

The relation for one entity and another entity of GIS is topological relation, and the set of two GIS entities with topological relation is an ordered pair, and so it is a binary relation. We use some characteristic of binary relation and rough set to study the relations for GIS entities, and we discover that the front, behind, upper and lower neighborhood relation of GIS entity have even more extensive sets than itself based on binary relation's generalized approximation space. We also discover that the upper and lower approximation of GIS entity for binary relation is more extensive than Pawlark rough set. We discover that the upper approximation for a single-point set of one GIS entity is equal to it's front neighborhood and is equal to it's behind neighborhood relation's inversion too.

Citation Download Citation

WeiHua Liao "The rough expression for topological relation of GIS entity based on binary relation", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78203E (20 August 2010); https://doi.org/10.1117/12.866308

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available