Support vector random field based approach towards object based classification of remotely sensed imagery

Author

Arun, P.V.

Author_Institution

Dept. of IT, RUB, Pheuntsholing, Bhutan

fYear

2013

fDate

26-28 Sept. 2013

Firstpage

1

Lastpage

4

Abstract

Remote sensing techniques are widely used for land cover classification and related analyses; however the availability of high resolution images have limited the accuracy of pixel based approaches. In this paper, we have analyzed the feasibility of incorporating contextual information to a support machine and have evaluated its performances with reference to the traditional approaches. Accuracy improvement of the proposed approach may be attributed to the effectiveness in combining spatial and spectral information.

Keywords

geophysical image processing; image classification; image resolution; remote sensing; support vector machines; contextual information; image resolution; land cover classification; object based classification; remote sensing techniques; remotely sensed imagery; spatial information; spectral information; support vector random field based approach; Accuracy; Classification algorithms; Hyperspectral sensors; Image resolution; Kernel; Support vector machines; Classification; Image Analysis; Vector Machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, Computing and Control (ISPCC), 2013 IEEE International Conference on

Conference_Location

Solan

Print_ISBN

978-1-4673-6188-0

Type

conf

DOI

10.1109/ISPCC.2013.6663455

Filename

6663455