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Conference Paper: Sparse representation for impervious surface area extraction using worldView-2 and TerraSAR-X data

TitleSparse representation for impervious surface area extraction using worldView-2 and TerraSAR-X data
Authors
KeywordsShadow
Index Term: Impervious
Sparse Representation
Issue Date2018
Citation
International Geoscience and Remote Sensing Symposium (IGARSS), 2018, v. 2018-July, p. 1660-1663 How to Cite?
Abstract© 2018 IEEE Not only the urbanization development but also its ecological process lays emphasis on the Impervious Surface Area (ISA) extraction, whereas, the ISA extraction from high-resolution images is challenging for both the phenomenon of the mixed pixels and shadow effects. To solve the problem, a Multi-Source Dictionary Sparse Representation Classification (MSD-SRC) method using WorldView-2 and TerraSAR-X dataset is proposed. First, it uses multi-source data and fuzzy samples by Low Pass Filtering (LPF) to solve the problem of road and building misclassification; second, learning Multi-Source Dictionary for non-shadow and shadow classes, then using discriminative sparse coding method for classification, therefore to reduce shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/277702

 

DC FieldValueLanguage
dc.contributor.authorLin, Yinyi-
dc.contributor.authorZhang, Hongsheng-
dc.contributor.authorLi, Gang-
dc.contributor.authorWang, Ting-
dc.contributor.authorLin, Hui-
dc.date.accessioned2019-09-27T08:29:45Z-
dc.date.available2019-09-27T08:29:45Z-
dc.date.issued2018-
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), 2018, v. 2018-July, p. 1660-1663-
dc.identifier.urihttp://hdl.handle.net/10722/277702-
dc.description.abstract© 2018 IEEE Not only the urbanization development but also its ecological process lays emphasis on the Impervious Surface Area (ISA) extraction, whereas, the ISA extraction from high-resolution images is challenging for both the phenomenon of the mixed pixels and shadow effects. To solve the problem, a Multi-Source Dictionary Sparse Representation Classification (MSD-SRC) method using WorldView-2 and TerraSAR-X dataset is proposed. First, it uses multi-source data and fuzzy samples by Low Pass Filtering (LPF) to solve the problem of road and building misclassification; second, learning Multi-Source Dictionary for non-shadow and shadow classes, then using discriminative sparse coding method for classification, therefore to reduce shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.-
dc.languageeng-
dc.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)-
dc.subjectShadow-
dc.subjectIndex Term: Impervious-
dc.subjectSparse Representation-
dc.titleSparse representation for impervious surface area extraction using worldView-2 and TerraSAR-X data-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IGARSS.2018.8517807-
dc.identifier.scopuseid_2-s2.0-85063125760-
dc.identifier.volume2018-July-
dc.identifier.spage1660-
dc.identifier.epage1663-

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