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Efficient remote sensing image retrieval based on building contours and graph similarity.

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Title: Efficient remote sensing image retrieval based on building contours and graph similarity.
Authors: Wang, Shiyuan1 (AUTHOR), Guo, Mingqiang1 (AUTHOR) guomingqiang@cug.edu.cn, Chen, Zhi1 (AUTHOR), Huang, Ying2 (AUTHOR), Cao, Wei1,3 (AUTHOR)
Source: International Journal of Remote Sensing. Jun2026, Vol. 47 Issue 11, p4792-4838. 47p.
Subjects: Image retrieval, Edges (Geometry), Urban research, Remote sensing, Land management, Image segmentation
Abstract: With the rapid growth of high-resolution remote sensing data, efficient and accurate image retrieval has become increasingly important for urban analysis and land management. Traditional retrieval approaches based on global or local features often suffer from low efficiency and limited robustness when applied to large-scale, multi-source datasets. To address these challenges, we propose a multi-scale image retrieval framework that integrates optimized building contour analysis with graph-based similarity measurement. Building footprints are first extracted using hybrid CNN – Transformer segmentation and then refined through a series of contour optimization steps, including adaptive simplification, rectification, and structural reconstruction. The optimized polygons are encoded by shape descriptors for preliminary screening, while a Siamese network further evaluates polygon similarity. At the final stage, building distribution is modelled as a labelled undirected graph, and an improved graph edit distance algorithm is applied for accurate matching. The framework was evaluated on Google, Bing, and ArcGIS satellite imagery as well as several public datasets. Experimental results demonstrate that the proposed method substantially improves retrieval accuracy and computational efficiency, while preserving geometric fidelity of complex urban structures. This study highlights the potential of contour- and graph-based approaches for advancing remote sensing image retrieval, with promising applications in urban planning, environmental monitoring, and disaster assessment. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 194221571
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Efficient remote sensing image retrieval based on building contours and graph similarity.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Shiyuan%22">Wang, Shiyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Mingqiang%22">Guo, Mingqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guomingqiang@cug.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhi%22">Chen, Zhi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Ying%22">Huang, Ying</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Wei%22">Cao, Wei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. Jun2026, Vol. 47 Issue 11, p4792-4838. 47p.
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  Data: <searchLink fieldCode="DE" term="%22Image+retrieval%22">Image retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Edges+%28Geometry%29%22">Edges (Geometry)</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+research%22">Urban research</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Land+management%22">Land management</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the rapid growth of high-resolution remote sensing data, efficient and accurate image retrieval has become increasingly important for urban analysis and land management. Traditional retrieval approaches based on global or local features often suffer from low efficiency and limited robustness when applied to large-scale, multi-source datasets. To address these challenges, we propose a multi-scale image retrieval framework that integrates optimized building contour analysis with graph-based similarity measurement. Building footprints are first extracted using hybrid CNN – Transformer segmentation and then refined through a series of contour optimization steps, including adaptive simplification, rectification, and structural reconstruction. The optimized polygons are encoded by shape descriptors for preliminary screening, while a Siamese network further evaluates polygon similarity. At the final stage, building distribution is modelled as a labelled undirected graph, and an improved graph edit distance algorithm is applied for accurate matching. The framework was evaluated on Google, Bing, and ArcGIS satellite imagery as well as several public datasets. Experimental results demonstrate that the proposed method substantially improves retrieval accuracy and computational efficiency, while preserving geometric fidelity of complex urban structures. This study highlights the potential of contour- and graph-based approaches for advancing remote sensing image retrieval, with promising applications in urban planning, environmental monitoring, and disaster assessment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/01431161.2026.2659873
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      – Code: eng
        Text: English
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        PageCount: 47
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    Subjects:
      – SubjectFull: Image retrieval
        Type: general
      – SubjectFull: Edges (Geometry)
        Type: general
      – SubjectFull: Urban research
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Land management
        Type: general
      – SubjectFull: Image segmentation
        Type: general
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      – TitleFull: Efficient remote sensing image retrieval based on building contours and graph similarity.
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            NameFull: Wang, Shiyuan
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            NameFull: Guo, Mingqiang
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            NameFull: Chen, Zhi
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            NameFull: Huang, Ying
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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