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Article: Improving Impervious Surface Extraction with Shadow-Based Sparse Representation from Optical, SAR, and LiDAR Data

TitleImproving Impervious Surface Extraction with Shadow-Based Sparse Representation from Optical, SAR, and LiDAR Data
Authors
KeywordsImpervious
multisource
shadow
sparse representation
Issue Date2019
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, v. 12, n. 7, p. 2417-2428 How to Cite?
Abstract© 2008-2012 IEEE. Numerous studies on environmental modeling and ecological process lay emphasis on the fundamental information of the impervious surface area (ISA). However, accurate ISA extraction from high-resolution satellite images remains challenging due to both the high diversity of land covers and shadow effects from tall buildings and trees. To address the problem, a discriminative Optical-SAR-LiDAR dictionary sparse representation classification (OSLD-SRC) method was proposed using high-resolution WorldView-2, GeoEye-1, TerraSAR-X, and airborne LiDAR data. First, it used multisource data and fuzzy samples by low-pass filtering (LPF) to solve the problem of roads and buildings misclassification; second, it learned the Optical-SAR-LiDAR dictionary for nonshadow and shadow classes, and then used discriminative sparse coding method for classification to reduce the shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/277709
ISSN
2017 Impact Factor: 2.777
2015 SCImago Journal Rankings: 1.196
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLin, Yinyi-
dc.contributor.authorZhang, Hongsheng-
dc.contributor.authorLi, Gang-
dc.contributor.authorWang, Ting-
dc.contributor.authorWan, Luoma-
dc.contributor.authorLin, Hui-
dc.date.accessioned2019-09-27T08:29:46Z-
dc.date.available2019-09-27T08:29:46Z-
dc.date.issued2019-
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, v. 12, n. 7, p. 2417-2428-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10722/277709-
dc.description.abstract© 2008-2012 IEEE. Numerous studies on environmental modeling and ecological process lay emphasis on the fundamental information of the impervious surface area (ISA). However, accurate ISA extraction from high-resolution satellite images remains challenging due to both the high diversity of land covers and shadow effects from tall buildings and trees. To address the problem, a discriminative Optical-SAR-LiDAR dictionary sparse representation classification (OSLD-SRC) method was proposed using high-resolution WorldView-2, GeoEye-1, TerraSAR-X, and airborne LiDAR data. First, it used multisource data and fuzzy samples by low-pass filtering (LPF) to solve the problem of roads and buildings misclassification; second, it learned the Optical-SAR-LiDAR dictionary for nonshadow and shadow classes, and then used discriminative sparse coding method for classification to reduce the shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.-
dc.languageeng-
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing-
dc.subjectImpervious-
dc.subjectmultisource-
dc.subjectshadow-
dc.subjectsparse representation-
dc.titleImproving Impervious Surface Extraction with Shadow-Based Sparse Representation from Optical, SAR, and LiDAR Data-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSTARS.2019.2907744-
dc.identifier.scopuseid_2-s2.0-85070519124-
dc.identifier.volume12-
dc.identifier.issue7-
dc.identifier.spage2417-
dc.identifier.epage2428-
dc.identifier.eissn2151-1535-
dc.identifier.isiWOS:000480354800040-

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