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Book Chapter: Integrating Change Vector Analysis, Post-Classification Comparison, and Object-Oriented Image Analysis for Land Use and Land Cover Change Detection Using RADARSAT-2 Polarimetric SAR Images

TitleIntegrating Change Vector Analysis, Post-Classification Comparison, and Object-Oriented Image Analysis for Land Use and Land Cover Change Detection Using RADARSAT-2 Polarimetric SAR Images
Authors
Issue Date2013
PublisherSpringer
Citation
Integrating Change Vector Analysis, Post-Classification Comparison, and Object-Oriented Image Analysis for Land Use and Land Cover Change Detection Using RADARSAT-2 Polarimetric SAR Images. In Timpf, S & Laube, P (Eds.), Advances in spatial data handling: geospatial dynamics, geosimulation and exploratory visualization, p. 107-123. Heidelberg ; New York: Springer, 2013 How to Cite?
AbstractThis study proposes a new method for land use and land cover (LULC) change detection using RADARSAT-2 polarimetric SAR (PolSAR) images. The proposed method combines change vector analysis (CVA) and post-classification analysis (PCC) to detect LULC changes using RADARSAT-2 PolSAR images based on object-oriented image analysis. A hierarchical segmentation was implemented on two RADARSAT-2 PolSAR images acquired at different times to delineate image objects. CVA was applied to the coherency matrix of PolSAR images to identify changed objects, and then PCC was used to determine the type of changes. The classification of the RADARSAT-2 images is based on the integration of polarimetric decomposition, object-oriented image analysis, decision tree algorithms, and support vector machines (SVMs). In comparison with the PCC that is based on the Wishart supervised classification, the proposed method improves the overall error rate for change detection and the overall accuracy for change type determination by 25.15 and 6.59 % respectively. The results show that the proposed method can achieve much higher accuracy for LULC change detection using RADARSAT-2 PolSAR images than the PCC that is based on the Wishart supervised classification.
Persistent Identifierhttp://hdl.handle.net/10722/188174
ISBN
Series/Report no.Advances in geographic information science

 

DC FieldValueLanguage
dc.contributor.authorQi, Zen_US
dc.contributor.authorYeh, AGOen_US
dc.date.accessioned2013-08-21T07:41:12Z-
dc.date.available2013-08-21T07:41:12Z-
dc.date.issued2013en_US
dc.identifier.citationIntegrating Change Vector Analysis, Post-Classification Comparison, and Object-Oriented Image Analysis for Land Use and Land Cover Change Detection Using RADARSAT-2 Polarimetric SAR Images. In Timpf, S & Laube, P (Eds.), Advances in spatial data handling: geospatial dynamics, geosimulation and exploratory visualization, p. 107-123. Heidelberg ; New York: Springer, 2013en_US
dc.identifier.isbn9783642323157-
dc.identifier.urihttp://hdl.handle.net/10722/188174-
dc.description.abstractThis study proposes a new method for land use and land cover (LULC) change detection using RADARSAT-2 polarimetric SAR (PolSAR) images. The proposed method combines change vector analysis (CVA) and post-classification analysis (PCC) to detect LULC changes using RADARSAT-2 PolSAR images based on object-oriented image analysis. A hierarchical segmentation was implemented on two RADARSAT-2 PolSAR images acquired at different times to delineate image objects. CVA was applied to the coherency matrix of PolSAR images to identify changed objects, and then PCC was used to determine the type of changes. The classification of the RADARSAT-2 images is based on the integration of polarimetric decomposition, object-oriented image analysis, decision tree algorithms, and support vector machines (SVMs). In comparison with the PCC that is based on the Wishart supervised classification, the proposed method improves the overall error rate for change detection and the overall accuracy for change type determination by 25.15 and 6.59 % respectively. The results show that the proposed method can achieve much higher accuracy for LULC change detection using RADARSAT-2 PolSAR images than the PCC that is based on the Wishart supervised classification.-
dc.languageengen_US
dc.publisherSpringer-
dc.relation.ispartofAdvances in spatial data handling: geospatial dynamics, geosimulation and exploratory visualizationen_US
dc.relation.ispartofseriesAdvances in geographic information science-
dc.titleIntegrating Change Vector Analysis, Post-Classification Comparison, and Object-Oriented Image Analysis for Land Use and Land Cover Change Detection Using RADARSAT-2 Polarimetric SAR Imagesen_US
dc.typeBook_Chapteren_US
dc.identifier.emailQi, Z: qizhixin@connect.hku.hken_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.identifier.doi10.1007/978-3-642-32316-4_8-
dc.identifier.hkuros218402en_US
dc.identifier.hkuros224948-
dc.identifier.spage107-
dc.identifier.epage123-
dc.publisher.placeHeidelberg ; New York-

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