Superpixel-based Markov random field for classification of hyperspectral images

Author

Shanshan Li ; Xiuping Jia ; Bing Zhang

Author_Institution

Inst. of Remote Sensing & Digital Earth, Beijing, China

fYear

2013

fDate

21-26 July 2013

Firstpage

3491

Lastpage

3494

Abstract

The paper presents a supervised classification method based on superpixels and Markov random field (MRF). Hyperspectral image is over-segmented into superpixels that are as basic unit of Markov random field instead of operating at the pixel level. Adaptive weight coefficient is introduced to determine contextual relationship between superpixels. Support vector machines are implemented for better estimation of spectral contribution to this approach. An experiment of real hyperspectral image reveals efficient performance.

Keywords

Markov processes; geophysical image processing; hyperspectral imaging; image classification; image segmentation; random processes; support vector machines; MRF; adaptive weight coefficient; hyperspectral image classification; image oversegmentation; spectral contribution estimation; superpixel-based Markov random field; supervised classification method; support vector machine; Accuracy; Hyperspectral imaging; Image classification; Markov random fields; Support vector machines; Hyperspectral; MRF; classification; superpixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International

Conference_Location

Melbourne, VIC

ISSN

2153-6996

Print_ISBN

978-1-4799-1114-1

Type

conf

DOI

10.1109/IGARSS.2013.6723581

Filename

6723581