Integration of Segmentation Techniques for Classification of Hyperspectral Images

Author

Ghamisi, Pedram ; Couceiro, Micael S. ; Fauvel, M. ; Atli Benediktsson, Jon

Author_Institution

Fac. of Electr. & Comput. Eng., Univ. of Iceland, Reykjavik, Iceland

Volume

11

Issue

1

fYear

2014

fDate

Jan. 2014

Firstpage

342

Lastpage

346

Abstract

A new spectral-spatial method for classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentation. The output of these two methods is classified by support vector machines. Experimental results indicate that the integration of the two segmentation methods can overcome the drawbacks of each other and increase the overall accuracy in classification.

Keywords

geophysical image processing; hyperspectral imaging; image classification; image segmentation; particle swarm optimisation; support vector machines; fractional-order Darwinian particle swarm optimization; hyperspectral image classification; hyperspectral image segmentation technique; mean shift segmentation; spectral-spatial method; support vector machine; Hyperspectral image analysis; mean shift segmentation; multilevel segmentation;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2013.2257675

Filename

6545298