Albedo recovery for hyperspectral image classification

Zhan, K; Wang, H; Xie, Y; Zhang, C; Min, Y

Albedo recovery for hyperspectral image classification

Zhan, K

Wang, H Xie, Y Zhang, C Min, Y

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Publication Type:: Journal Article
Citation:: Journal of Electronic Imaging, 2017, 26 (4)
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Zhan, K https://orcid.org/0000-0002-8000-5682	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Xie, Y	en_US
dc.contributor.author	Zhang, C	en_US
dc.contributor.author	Min, Y	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Journal of Electronic Imaging, 2017, 26 (4)	en_US
dc.identifier.issn	1017-9909	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126137
dc.description.abstract	© 2017 SPIE and IS & T. Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials. We, therefore, recover the intrinsic albedo feature of the hyperspectral image to exploit the spatial semantic information. Then, we use the support vector machine (SVM) to classify the recovered intrinsic albedo hyperspectral image. The SVM tries to maximize the minimum margin to achieve good generalization performance. Experimental results show that the SVM with the intrinsic albedo feature method achieves a better classification performance than the state-of-the-art methods in terms of visual quality and three quantitative metrics.	en_US
dc.relation.ispartof	Journal of Electronic Imaging	en_US
dc.relation.isbasedon	10.1117/1.JEI.26.4.043010	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Albedo recovery for hyperspectral image classification	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 SPIE and IS & T. Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials. We, therefore, recover the intrinsic albedo feature of the hyperspectral image to exploit the spatial semantic information. Then, we use the support vector machine (SVM) to classify the recovered intrinsic albedo hyperspectral image. The SVM tries to maximize the minimum margin to achieve good generalization performance. Experimental results show that the SVM with the intrinsic albedo feature method achieves a better classification performance than the state-of-the-art methods in terms of visual quality and three quantitative metrics.

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http://hdl.handle.net/10453/126137