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Article: A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

TitleA novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data
Authors
KeywordsDecision tree
Land use classification
Object-oriented method
Polarimetric interferometric SAR
Polarimetric SAR
Issue Date2012
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse
Citation
Remote Sensing Of Environment, 2012, v. 118, p. 21-39 How to Cite?
AbstractThis study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data. © 2011 Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/166094
ISSN
2014 Impact Factor: 6.393
2013 SCImago Journal Rankings: 3.190
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQi, Zen_HK
dc.contributor.authorYeh, AGOen_HK
dc.contributor.authorLi, Xen_HK
dc.contributor.authorLin, Zen_HK
dc.date.accessioned2012-09-20T08:28:14Z-
dc.date.available2012-09-20T08:28:14Z-
dc.date.issued2012en_HK
dc.identifier.citationRemote Sensing Of Environment, 2012, v. 118, p. 21-39en_HK
dc.identifier.issn0034-4257en_HK
dc.identifier.urihttp://hdl.handle.net/10722/166094-
dc.description.abstractThis study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data. © 2011 Elsevier Inc.en_HK
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rseen_HK
dc.relation.ispartofRemote Sensing of Environmenten_HK
dc.subjectDecision treeen_HK
dc.subjectLand use classificationen_HK
dc.subjectObject-oriented methoden_HK
dc.subjectPolarimetric interferometric SARen_HK
dc.subjectPolarimetric SARen_HK
dc.titleA novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0034-4257&volume=118&spage=21&epage=39&date=2012&atitle=A+Novel+Algorithm+for+Land+Use+and+Land+Cover+Classification+Using+RADARSAT-2+Polarimetric+SAR+Dataen_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AGO=rp01033en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2011.11.001en_HK
dc.identifier.scopuseid_2-s2.0-82655178425en_HK
dc.identifier.hkuros210515en_US
dc.identifier.hkuros218397-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-82655178425&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume118en_HK
dc.identifier.spage21en_HK
dc.identifier.epage39en_HK
dc.identifier.eissn1879-0704-
dc.identifier.isiWOS:000300517700003-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridQi, Z=35307702300en_HK
dc.identifier.scopusauthoridYeh, AGO=7103069369en_HK
dc.identifier.scopusauthoridLi, X=26660668800en_HK
dc.identifier.scopusauthoridLin, Z=36816043100en_HK
dc.identifier.citeulike10121175-

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