Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification

Liu, C; Zhou, J; Liang, J; Qian, Y; Li, H; Gao, Y

Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification

Liu, C Zhou, J Liang, J

Qian, Y Li, H Gao, Y

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10, (3), pp. 1151-1164
Issue Date:: 2017-03-01

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Field	Value	Language
dc.contributor.author	Liu, C
dc.contributor.author	Zhou, J
dc.contributor.author	Liang, J https://orcid.org/0000-0001-7179-5208
dc.contributor.author	Qian, Y
dc.contributor.author	Li, H
dc.contributor.author	Gao, Y
dc.date.accessioned	2022-07-13T01:12:50Z
dc.date.available	2022-07-13T01:12:50Z
dc.date.issued	2017-03-01
dc.identifier.citation	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10, (3), pp. 1151-1164
dc.identifier.issn	1939-1404
dc.identifier.issn	2151-1535
dc.identifier.uri	http://hdl.handle.net/10453/158820
dc.description.abstract	In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse coding methods. First, the distribution of different ground objects leads to different coding coefficients across the spatial locations. Second, local spatial structures change slightly across bands due to different reflectance properties of various object ma- terials. Finally and more importantly, some sort of structural consistency shall be enforced across bands to reflect the fact that the same object appears at the same spatial location in all bands of an image. Based on these considerations, we propose a novel joint spectral-spatial sparse coding model that explores structural consistency for hyperspectral image classification. For each band im- age, we adopt a sparse coding step to reconstruct the structures in the band image. This allows different dictionaries be generated to characterize the band-wise image variation. At the same time, we enforce the same coding coefficients at the same spatial location in different bands so as to maintain consistent structures across bands. To further promote the discriminating power of the model, we incorporate a graph Laplacian sparsity constraint into the model to ensure spectral consistency in the dictionary generation step. Experimental results show that the proposed method outperforms some state-of-the-art spectral-spatial sparse coding methods.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.isbasedon	10.1109/JSTARS.2016.2602305
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0801 Artificial Intelligence and Image Processing, 0909 Geomatic Engineering
dc.title	Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-13T01:12:48Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	3

Abstract:

In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse coding methods. First, the distribution of different ground objects leads to different coding coefficients across the spatial locations. Second, local spatial structures change slightly across bands due to different reflectance properties of various object ma- terials. Finally and more importantly, some sort of structural consistency shall be enforced across bands to reflect the fact that the same object appears at the same spatial location in all bands of an image. Based on these considerations, we propose a novel joint spectral-spatial sparse coding model that explores structural consistency for hyperspectral image classification. For each band im- age, we adopt a sparse coding step to reconstruct the structures in the band image. This allows different dictionaries be generated to characterize the band-wise image variation. At the same time, we enforce the same coding coefficients at the same spatial location in different bands so as to maintain consistent structures across bands. To further promote the discriminating power of the model, we incorporate a graph Laplacian sparsity constraint into the model to ensure spectral consistency in the dictionary generation step. Experimental results show that the proposed method outperforms some state-of-the-art spectral-spatial sparse coding methods.

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http://hdl.handle.net/10453/158820