Texture Feature Fusion for High Resolution Satellite Image Classification

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International Conference on Computer Graphics, Imaging and Visualization (CGIV'05)

Beijing, China

July 26-July 29

ISBN: 0-7695-2392-7

	ASCII Text	x
	Yindi Zhao, Liangpei Zhang, Pingxiang Li, "Texture Feature Fusion for High Resolution Satellite Image Classification," Computer Graphics, Imaging and Visualization, International Conference on, pp. 19-23, International Conference on Computer Graphics, Imaging and Visualization (CGIV'05), 2005.

	BibTex	x
	@article{ 10.1109/CGIV.2005.76, author = {Yindi Zhao and Liangpei Zhang and Pingxiang Li}, title = {Texture Feature Fusion for High Resolution Satellite Image Classification}, journal ={Computer Graphics, Imaging and Visualization, International Conference on}, volume = {0}, year = {2005}, isbn = {0-7695-2392-7}, pages = {19-23}, doi = {http://doi.ieeecomputersociety.org/10.1109/CGIV.2005.76}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer Graphics, Imaging and Visualization, International Conference on TI - Texture Feature Fusion for High Resolution Satellite Image Classification SN - 0-7695-2392-7 SP19 EP23 A1 - Yindi Zhao, A1 - Liangpei Zhang, A1 - Pingxiang Li, PY - 2005 KW - null VL - 0 JA - Computer Graphics, Imaging and Visualization, International Conference on ER -

Yindi Zhao, Wuhan University

Liangpei Zhang, Wuhan University

Pingxiang Li, Wuhan University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CGIV.2005.76

Multi-channel Gabor filters (MCGF) and Markov random fields (MRF) have been demonstrated to be quite effective for texture analysis. In this paper, MCGF and MRF features are respectively extracted from input texture images by means of the two above techniques. A MCGF/MRF feature fusion algorithm for texture classification is proposed. The fused MCGF/MRF features achieved by this novel algorithm have much higher discrimination than either the pure features or the combined features without selection, according to the Fisher criterion and classification accuracy. The stability and effectiveness of the proposed algorithm are verified on samples of Brodatz and QuickBird images.

Citation:

Yindi Zhao, Liangpei Zhang, Pingxiang Li, "Texture Feature Fusion for High Resolution Satellite Image Classification," cgiv, pp.19-23, International Conference on Computer Graphics, Imaging and Visualization (CGIV'05), 2005

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