Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification

Shen, J; Cao, X; Li, Y; Xu, D

Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification

Shen, J Cao, X Li, Y Xu, D

Permalink

Publication Type:: Journal Article
Citation:: IEEE Geoscience and Remote Sensing Letters, 2018, 15 (4), pp. 622 - 626
Issue Date:: 2018-04-01

Closed Access

	Filename	Description	Size
	08291065.pdf	Published Version	521.76 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shen, J	en_US
dc.contributor.author	Cao, X	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	IEEE Geoscience and Remote Sensing Letters, 2018, 15 (4), pp. 622 - 626	en_US
dc.identifier.issn	1545-598X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131891
dc.description.abstract	© 2012 IEEE. Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called hyperspectral feature adaptation and augmentation (HFAA) for cross-scene HSI classification. The proposed HFAA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. To further enhance the common subspace representation, we propose to augment it by the feature selection strategy. HFAA can make full use of the original features from both source and target domains, and increase the similarity of the samples with the same label from the two domains. Our proposed HFAA method achieves compact but discriminative feature representations, which make it well suited for data sets with a large number of classes and huge interclass ambiguity. Experimental results on the Earth Observing 1 hyperspectral data set show that HFAA can produce state-of-the-art performance and surpass previous methods.	en_US
dc.relation.ispartof	IEEE Geoscience and Remote Sensing Letters	en_US
dc.relation.isbasedon	10.1109/LGRS.2018.2799628	en_US
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	15	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	15	en_US

Abstract:

© 2012 IEEE. Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called hyperspectral feature adaptation and augmentation (HFAA) for cross-scene HSI classification. The proposed HFAA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. To further enhance the common subspace representation, we propose to augment it by the feature selection strategy. HFAA can make full use of the original features from both source and target domains, and increase the similarity of the samples with the same label from the two domains. Our proposed HFAA method achieves compact but discriminative feature representations, which make it well suited for data sets with a large number of classes and huge interclass ambiguity. Experimental results on the Earth Observing 1 hyperspectral data set show that HFAA can produce state-of-the-art performance and surpass previous methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/131891