A spatial data model for remote sensing image retrieval

Author

Akcay, H.G. ; Aksoy, Selim

Author_Institution

Bilgisayar Muhendisligi Bolumu, Bilkent Univ., Ankara, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

4

Abstract

Given a query region, our aim is to discover and retrieve regions with similar spatial arrangement and characteristics in other areas of the same large image or in other images. A Markov random field is constructed by representing regions as variables and connecting the vertices that are spatially close by edges. Then, a maximum entropy distribution is assumed over the query region process and retrieval of the similar region processes on the target image is achieved according to their probability. Experiments using WorldView-2 images show that statistical modelling of compound structures enable high-level and large-scale retrieval applications.

Keywords

Markov processes; geophysical image processing; image representation; image retrieval; remote sensing; statistical analysis; Markov random field; WorldView-2 images; compound structure statistical modelling; high-level retrieval applications; large-scale retrieval applications; maximum entropy distribution; query region process; region representation; remote sensing image retrieval; spatial data model; Compounds; Data models; Image retrieval; Markov processes; Remote sensing; Spatial databases; Image retrieval; Markov random field; spatial arrangements;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531469

Filename

6531469