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Integration of Segmentation Techniques for Classification of Hyperspectral Images

Integration of Segmentation Techniques for Classification of Hyperspectral Images


Titill: Integration of Segmentation Techniques for Classification of Hyperspectral Images
Höfundur: Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Ghamisi, Pedram
Couceiro, Micael S.
Fauvel, Mathieu
Útgáfa: 2014
Tungumál: Enska
Umfang: 342-346
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
Birtist í: IEEE Geoscience and Remote Sensing Letters; 11(1)
ISSN: 1545-598X
DOI: 10.1109/LGRS.2013.2257675
Efnisorð: Support vector machines; Geophysical image processing; Hyperspectral imaging; Image classification
URI: https://hdl.handle.net/20.500.11815/138

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Tilvitnun:

P. Ghamisi, M. S. Couceiro, M. Fauvel and J. A. Benediktsson. (2014). Integration of Segmentation Techniques for Classification of Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters, 11(1), 342-346

Útdráttur:

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.

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