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Conference Paper: Using projection pursuit learning network architecture to detect land use changes

TitleUsing projection pursuit learning network architecture to detect land use changes
Authors
KeywordsChange detection
ETM image
Land use
Projection pursuit learning network
Issue Date2008
Citation
Proceedings of SPIE - The International Society for Optical Engineering, 2008, v. 7144, article no. 71440H How to Cite?
AbstractA robust method to conduct land use change detection between multi-temporal images using projection pursuit learning network architecture (PPLNA) is proposed. The method uses a parallel approach that includes three different PPLNs: two of them are used to generate the change map using the multi-spectral information, while the third produces a change mask exploiting multi-temporality. The distinctive feature and major merit of PPLNA from traditional neural network for land use change detection are the proposed method simultaneously exploits both the post classification of multi-spectral and multi-temporal information that is associated with the changes values of the pixel spectral reflectance, and hence improve the change detection accuracies. To validate the performance of the proposed method, the experiments using the ETM+ images for the area of Calgary have been carried out. The accuracies of the final classification and change detection maps have been evaluated with ground truth comparisons. The experimental result demonstrates that the proposed method achieves better accuracies. © 2008 SPIE.
Persistent Identifierhttp://hdl.handle.net/10722/330114
ISSN
2023 SCImago Journal Rankings: 0.152

 

DC FieldValueLanguage
dc.contributor.authorWu, Bo-
dc.contributor.authorHuang, Bo-
dc.contributor.authorYan, Yong-
dc.date.accessioned2023-08-09T03:37:53Z-
dc.date.available2023-08-09T03:37:53Z-
dc.date.issued2008-
dc.identifier.citationProceedings of SPIE - The International Society for Optical Engineering, 2008, v. 7144, article no. 71440H-
dc.identifier.issn0277-786X-
dc.identifier.urihttp://hdl.handle.net/10722/330114-
dc.description.abstractA robust method to conduct land use change detection between multi-temporal images using projection pursuit learning network architecture (PPLNA) is proposed. The method uses a parallel approach that includes three different PPLNs: two of them are used to generate the change map using the multi-spectral information, while the third produces a change mask exploiting multi-temporality. The distinctive feature and major merit of PPLNA from traditional neural network for land use change detection are the proposed method simultaneously exploits both the post classification of multi-spectral and multi-temporal information that is associated with the changes values of the pixel spectral reflectance, and hence improve the change detection accuracies. To validate the performance of the proposed method, the experiments using the ETM+ images for the area of Calgary have been carried out. The accuracies of the final classification and change detection maps have been evaluated with ground truth comparisons. The experimental result demonstrates that the proposed method achieves better accuracies. © 2008 SPIE.-
dc.languageeng-
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering-
dc.subjectChange detection-
dc.subjectETM image-
dc.subjectLand use-
dc.subjectProjection pursuit learning network-
dc.titleUsing projection pursuit learning network architecture to detect land use changes-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.812707-
dc.identifier.scopuseid_2-s2.0-62649126152-
dc.identifier.volume7144-
dc.identifier.spagearticle no. 71440H-
dc.identifier.epagearticle no. 71440H-

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