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Article: Applying an anomaly-detection algorithm for short-term land use and land cover change detection using time-series SAR images

TitleApplying an anomaly-detection algorithm for short-term land use and land cover change detection using time-series SAR images
Authors
Issue Date2010
Citation
Giscience And Remote Sensing, 2010, v. 47 n. 3, p. 379-397 How to Cite?
AbstractIn this study, short-term land use and land cover (LULC) changes caused by human activity were considered as spatial-temporal abnormalities in time-series images. A density-based anomaly detection (DBAD) algorithm was designed to detect the changes. Then the algorithm was applied to RADARSAT time-series images, and synchronous field surveying was performed for validation. The results showed that the DBAD algorithm was good at detecting in-progress construction and newly builtup parcels, with an error of less than 13.3%. A lower detection error was achieved for woodland areas, and a larger error for built-up areas and for some mixed-use land parcels due to the complexity of the parcels.
Persistent Identifierhttp://hdl.handle.net/10722/176296
ISSN
2015 Impact Factor: 2.482
2015 SCImago Journal Rankings: 1.127
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQian, Jen_US
dc.contributor.authorLi, Xen_US
dc.contributor.authorLiao, Sen_US
dc.contributor.authorYeh, AGOen_US
dc.date.accessioned2012-11-26T09:08:17Z-
dc.date.available2012-11-26T09:08:17Z-
dc.date.issued2010en_US
dc.identifier.citationGiscience And Remote Sensing, 2010, v. 47 n. 3, p. 379-397en_US
dc.identifier.issn1548-1603en_US
dc.identifier.urihttp://hdl.handle.net/10722/176296-
dc.description.abstractIn this study, short-term land use and land cover (LULC) changes caused by human activity were considered as spatial-temporal abnormalities in time-series images. A density-based anomaly detection (DBAD) algorithm was designed to detect the changes. Then the algorithm was applied to RADARSAT time-series images, and synchronous field surveying was performed for validation. The results showed that the DBAD algorithm was good at detecting in-progress construction and newly builtup parcels, with an error of less than 13.3%. A lower detection error was achieved for woodland areas, and a larger error for built-up areas and for some mixed-use land parcels due to the complexity of the parcels.en_US
dc.languageengen_US
dc.relation.ispartofGIScience and Remote Sensingen_US
dc.titleApplying an anomaly-detection algorithm for short-term land use and land cover change detection using time-series SAR imagesen_US
dc.typeArticleen_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.2747/1548-1603.47.3.379en_US
dc.identifier.scopuseid_2-s2.0-77956337678en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956337678&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume47en_US
dc.identifier.issue3en_US
dc.identifier.spage379en_US
dc.identifier.epage397en_US
dc.identifier.isiWOS:000281404300005-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridQian, J=18936748300en_US
dc.identifier.scopusauthoridLi, X=34872691500en_US
dc.identifier.scopusauthoridLiao, S=7401923181en_US
dc.identifier.scopusauthoridYeh, AGO=7103069369en_US

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