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Article: A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data
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TitleA novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data
 
AuthorsQi, Z1
Yeh, AGO1
Li, X2
Lin, Z2
 
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
 
CitationRemote Sensing Of Environment, 2012, v. 118, p. 21-39 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.rse.2011.11.001
 
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.
 
ISSN0034-4257
2013 Impact Factor: 4.769
2013 SCImago Journal Rankings: 3.190
 
DOIhttp://dx.doi.org/10.1016/j.rse.2011.11.001
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorQi, Z
 
dc.contributor.authorYeh, AGO
 
dc.contributor.authorLi, X
 
dc.contributor.authorLin, Z
 
dc.date.accessioned2012-09-20T08:28:14Z
 
dc.date.available2012-09-20T08:28:14Z
 
dc.date.issued2012
 
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.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationRemote Sensing Of Environment, 2012, v. 118, p. 21-39 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.rse.2011.11.001
 
dc.identifier.citeulike10121175
 
dc.identifier.doihttp://dx.doi.org/10.1016/j.rse.2011.11.001
 
dc.identifier.eissn1879-0704
 
dc.identifier.epage39
 
dc.identifier.hkuros210515
 
dc.identifier.hkuros218397
 
dc.identifier.issn0034-4257
2013 Impact Factor: 4.769
2013 SCImago Journal Rankings: 3.190
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-82655178425
 
dc.identifier.spage21
 
dc.identifier.urihttp://hdl.handle.net/10722/166094
 
dc.identifier.volume118
 
dc.languageeng
 
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse
 
dc.publisher.placeUnited States
 
dc.relation.ispartofRemote Sensing of Environment
 
dc.relation.referencesReferences in Scopus
 
dc.subjectDecision tree
 
dc.subjectLand use classification
 
dc.subjectObject-oriented method
 
dc.subjectPolarimetric interferometric SAR
 
dc.subjectPolarimetric SAR
 
dc.titleA novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Sun Yat-Sen University