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Article: A modified PSO algorithm for remote sensing image template matching

TitleA modified PSO algorithm for remote sensing image template matching
Authors
Issue Date2010
Citation
Photogrammetric Engineering and Remote Sensing, 2010, v. 76, n. 4, p. 379-389 How to Cite?
AbstractImage template matching is essential in image analysis and computer vision tasks. Cross-correlation algorithms are often used in practice, but they are sensitive to nonlinear changes in image intensity and random noise, and are computationally expensive. In this paper, we propose a template-matching algorithm based on a modified particle swarm optimization (PSO) procedure with a mutual information (Ml) similarity measure. The influence of Ml on the performance of template matching, calculated by different histogram bins, is analyzed first. A modified PSO method (CRI-PSO) is then presented. The proposed algorithm is tested with remote sensing imagery from different sensors and for different seasons. Our experimental results indicate that the proposed approach is robust in practical scenarios and outperforms the standard PSO, multi-start PSO, and cross-correlation algorithms in accuracy and efficiency with our test data. The proposed method can be used for position estimation of aircraft, object recognition, and image retrieval. © 2010 American Society for Photogrammetry and Remote Sensing.
Persistent Identifierhttp://hdl.handle.net/10722/296660
ISSN
2023 Impact Factor: 1.0
2023 SCImago Journal Rankings: 0.309
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorAn, Ru-
dc.contributor.authorGong, Peng-
dc.contributor.authorWang, Hullin-
dc.contributor.authorFeng, Xuezhl-
dc.contributor.authorXiao, Pengfeng-
dc.contributor.authorChen, Qi-
dc.contributor.authorZhang, Qing-
dc.contributor.authorChen, Chunye-
dc.contributor.authorYan, Peng-
dc.date.accessioned2021-02-25T15:16:23Z-
dc.date.available2021-02-25T15:16:23Z-
dc.date.issued2010-
dc.identifier.citationPhotogrammetric Engineering and Remote Sensing, 2010, v. 76, n. 4, p. 379-389-
dc.identifier.issn0099-1112-
dc.identifier.urihttp://hdl.handle.net/10722/296660-
dc.description.abstractImage template matching is essential in image analysis and computer vision tasks. Cross-correlation algorithms are often used in practice, but they are sensitive to nonlinear changes in image intensity and random noise, and are computationally expensive. In this paper, we propose a template-matching algorithm based on a modified particle swarm optimization (PSO) procedure with a mutual information (Ml) similarity measure. The influence of Ml on the performance of template matching, calculated by different histogram bins, is analyzed first. A modified PSO method (CRI-PSO) is then presented. The proposed algorithm is tested with remote sensing imagery from different sensors and for different seasons. Our experimental results indicate that the proposed approach is robust in practical scenarios and outperforms the standard PSO, multi-start PSO, and cross-correlation algorithms in accuracy and efficiency with our test data. The proposed method can be used for position estimation of aircraft, object recognition, and image retrieval. © 2010 American Society for Photogrammetry and Remote Sensing.-
dc.languageeng-
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensing-
dc.titleA modified PSO algorithm for remote sensing image template matching-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.14358/PERS.76.4.379-
dc.identifier.scopuseid_2-s2.0-77950994951-
dc.identifier.volume76-
dc.identifier.issue4-
dc.identifier.spage379-
dc.identifier.epage389-
dc.identifier.isiWOS:000276231900006-
dc.identifier.issnl0099-1112-

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