A multifeature tensor for remote-sensing target recognition

Zhang, L; Tao, D; Huang, X

A multifeature tensor for remote-sensing target recognition

Zhang, L Tao, D

Huang, X

Permalink

Publication Type:: Journal Article
Citation:: IEEE Geoscience and Remote Sensing Letters, 2011, 8 (2), pp. 374 - 378
Issue Date:: 2011-03-01

Closed Access

	Filename	Description	Size
	2010004740OK.pdf		291.27 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	Zhang, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Huang, X	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	IEEE Geoscience and Remote Sensing Letters, 2011, 8 (2), pp. 374 - 378	en_US
dc.identifier.issn	1545-598X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15374
dc.description.abstract	In remote-sensing image target recognition, the target or background object is usually transformed to a feature vector, such as a spectral feature vector. However, this kind of vector represents only one pixel of a remote-sensing image that considers the spectral information but ignores the spatial relationship of neighboring pixels (i.e., the local texture and structure). In this letter, we propose a new way to represent an image object as a multifeature tensor that encodes both the spectral and textural information (Gabor function) and then apply the support tensor machine for target recognition. A range of experiments demonstrates that the effectiveness of the proposed method can deliver a high and correct recognition rate with a small number of training samples. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Geoscience and Remote Sensing Letters	en_US
dc.relation.isbasedon	10.1109/LGRS.2010.2077272	en_US
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	A multifeature tensor for remote-sensing target recognition	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	8	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	WOS:000287659300039	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

In remote-sensing image target recognition, the target or background object is usually transformed to a feature vector, such as a spectral feature vector. However, this kind of vector represents only one pixel of a remote-sensing image that considers the spectral information but ignores the spatial relationship of neighboring pixels (i.e., the local texture and structure). In this letter, we propose a new way to represent an image object as a multifeature tensor that encodes both the spectral and textural information (Gabor function) and then apply the support tensor machine for target recognition. A range of experiments demonstrates that the effectiveness of the proposed method can deliver a high and correct recognition rate with a small number of training samples. © 2006 IEEE.

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

http://hdl.handle.net/10453/15374