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Article: Determining class proportions within a pixel using a new mixed-label analysis method

TitleDetermining class proportions within a pixel using a new mixed-label analysis method
Authors
KeywordsMixed pixels
Mixed-label analysis (MLA)
Remote sensing
Soft classification
Issue Date2010
PublisherIEEE.
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2010, v. 48 n. 4, p. 1882-1891 How to Cite?
AbstractLand-cover classification is perhaps one of the most important applications of remote-sensing data. There are limitations with conventional (hard) classification methods because mixed pixels are often abundant in remote-sensing images, and they cannot be appropriately or accurately classified by these methods. This paper presents a new approach in improving the classification performance of remote-sensing applications based on mixed-label analysis (MLA). This MLA model can determine class proportions within a pixel in producing soft classification from remote-sensing data. Simulated images and real data sets are used to illustrate the simplicity and effectiveness of this proposed approach. Classification accuracy achieved by MLA is compared with other conventional methods such as linear spectral mixture models, maximum likelihood, minimum distance, and artificial neural networks. Experiments have demonstrated that this new method can generate more accurate land-cover maps, even in the presence of uncertainties in the form of mixed pixels.
Persistent Identifierhttp://hdl.handle.net/10722/131084
ISSN
2015 Impact Factor: 3.36
2015 SCImago Journal Rankings: 1.975
ISI Accession Number ID
Funding AgencyGrant Number
National Natural Science Foundation of China40901187
Key National Natural Science Foundation of China40830532
National Outstanding Youth Foundation of China40525002
Guangdong Provincial Natural Science Foundation of China9451027501002471
Research Fund of LREIS, CAS4106298
Funding Information:

This work was supported in part by the National Natural Science Foundation of China under Grant 40901187, by the Key National Natural Science Foundation of China under Grant 40830532, by the National Outstanding Youth Foundation of China under Grant 40525002, by the Guangdong Provincial Natural Science Foundation of China under Grant 9451027501002471, and by the Research Fund of LREIS, CAS, under Grant 4106298.

 

DC FieldValueLanguage
dc.contributor.authorLiu, X-
dc.contributor.authorLi, X-
dc.contributor.authorZhang, X-
dc.date.accessioned2011-01-24T06:31:41Z-
dc.date.available2011-01-24T06:31:41Z-
dc.date.issued2010-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2010, v. 48 n. 4, p. 1882-1891-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/131084-
dc.description.abstractLand-cover classification is perhaps one of the most important applications of remote-sensing data. There are limitations with conventional (hard) classification methods because mixed pixels are often abundant in remote-sensing images, and they cannot be appropriately or accurately classified by these methods. This paper presents a new approach in improving the classification performance of remote-sensing applications based on mixed-label analysis (MLA). This MLA model can determine class proportions within a pixel in producing soft classification from remote-sensing data. Simulated images and real data sets are used to illustrate the simplicity and effectiveness of this proposed approach. Classification accuracy achieved by MLA is compared with other conventional methods such as linear spectral mixture models, maximum likelihood, minimum distance, and artificial neural networks. Experiments have demonstrated that this new method can generate more accurate land-cover maps, even in the presence of uncertainties in the form of mixed pixels.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.rightsIEEE Transactions on Geoscience and Remote Sensing. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectMixed pixels-
dc.subjectMixed-label analysis (MLA)-
dc.subjectRemote sensing-
dc.subjectSoft classification-
dc.titleDetermining class proportions within a pixel using a new mixed-label analysis methoden_US
dc.typeArticleen_US
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0196-2892&volume=48&issue=4&spage=1882&epage=1891&date=2010&atitle=Determining+class+proportions+within+a+pixel+using+a+new+mixed-label+analysis+method-
dc.identifier.emailLiu, X: yiernanh@163.com-
dc.identifier.emailLi, X: lixia@mail.sysu.edu.cn-
dc.identifier.emailZhang, X: xiaohu.zhang.cn@gmail.com-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TGRS.2009.2033178-
dc.identifier.scopuseid_2-s2.0-79952070511-
dc.identifier.hkuros182517-
dc.identifier.volume48-
dc.identifier.issue4-
dc.identifier.spage1882-
dc.identifier.epage1891-
dc.identifier.isiWOS:000276014800022-

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