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Article: Prior-knowledge-based spectral mixture analysis for impervious surface mapping

TitlePrior-knowledge-based spectral mixture analysis for impervious surface mapping
Authors
KeywordsImpervious surface
Prior-knowledge
Spectral mixture analysis
V-I-S
Issue Date2014
Citation
International Journal of Applied Earth Observation and Geoinformation, 2014, v. 28, n. 1, p. 201-210 How to Cite?
AbstractIn this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the vegetation-impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the vegetation-impervious-soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, low albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% only using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas. © 2013 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/329317
ISSN
2023 Impact Factor: 7.6
2023 SCImago Journal Rankings: 2.108
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Jinshui-
dc.contributor.authorHe, Chunyang-
dc.contributor.authorZhou, Yuyu-
dc.contributor.authorZhu, Shuang-
dc.contributor.authorShuai, Guanyuan-
dc.date.accessioned2023-08-09T03:31:56Z-
dc.date.available2023-08-09T03:31:56Z-
dc.date.issued2014-
dc.identifier.citationInternational Journal of Applied Earth Observation and Geoinformation, 2014, v. 28, n. 1, p. 201-210-
dc.identifier.issn1569-8432-
dc.identifier.urihttp://hdl.handle.net/10722/329317-
dc.description.abstractIn this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the vegetation-impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the vegetation-impervious-soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, low albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% only using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas. © 2013 Elsevier B.V.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Applied Earth Observation and Geoinformation-
dc.subjectImpervious surface-
dc.subjectPrior-knowledge-
dc.subjectSpectral mixture analysis-
dc.subjectV-I-S-
dc.titlePrior-knowledge-based spectral mixture analysis for impervious surface mapping-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jag.2013.12.001-
dc.identifier.scopuseid_2-s2.0-84897371803-
dc.identifier.volume28-
dc.identifier.issue1-
dc.identifier.spage201-
dc.identifier.epage210-
dc.identifier.eissn1872-826X-
dc.identifier.isiWOS:000332429000019-

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