Iterative Global Mapping-Local Searching for Heterogeneous Change Detection with Unregistered Images.
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| Title: | Iterative Global Mapping-Local Searching for Heterogeneous Change Detection with Unregistered Images. |
|---|---|
| Authors: | Sun, Yuli1,2 (AUTHOR) sunyuli@mail.ustc.edu.cn, Wu, Junzheng3 (AUTHOR), Zhang, Han3 (AUTHOR), Li, Zhang2 (AUTHOR), Lei, Lin1 (AUTHOR), Kuang, Gangyao1 (AUTHOR) kuanggangyao@nudt.edu.cn |
| Source: | International Journal of Computer Vision. Apr2026, Vol. 134 Issue 4, p1-22. 22p. |
| Subjects: | Image registration, Markov random fields, Iterative methods (Mathematics), Remote sensing, Change-point problems |
| Abstract: | Heterogeneous change detection (HeCD) is a highly valuable yet challenging task in remote sensing. However, existing HeCD methods primarily focus on well-registered images, without considering unregistered heterogeneous image pairs, which are more common in real-world applications. In this paper, we first analyze why unregistered images significantly complicate the HeCD problem: they not only cause boundary errors in change detection but also undermine the image transformation process required for making heterogeneous images comparable. These effects are further amplified by the intrinsic complexity of HeCD. In light of this, we propose an unsupervised iterative global mapping-local searching method (IGmLs) for HeCD subject to registration errors. Specifically, IGmLs utilizes the global mapping to transform images into a common structural space to enable the comparison of heterogeneous images, and uses the local searching to reduce the direct influence of registration errors on change metrics, which is based on the analysis that misregistration would cause an unwanted increment on the change metric in unchanged region. Then, IGmLs builds a Markov random field (MRF) model to combine the global mapping and local searching processes, which enhances the robustness to misregistrations by considering spatial correlations. Finally, an iterative framework is employed to backpropagate the matching and changing results to refine the global mapping and local searching processes, which further eliminates the indirect influence of changes and misregistration on the image transformation and change metrics. Extensive experiments on five datasets have demonstrated the effectiveness of the proposed IGmLs. The codes will be released at https://github.com/yulisun/IGmLs. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision is the property of Springer Nature 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192202275 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Iterative Global Mapping-Local Searching for Heterogeneous Change Detection with Unregistered Images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Yuli%22">Sun, Yuli</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sunyuli@mail.ustc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Junzheng%22">Wu, Junzheng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Han%22">Zhang, Han</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhang%22">Li, Zhang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lei%2C+Lin%22">Lei, Lin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuang%2C+Gangyao%22">Kuang, Gangyao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kuanggangyao@nudt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Apr2026, Vol. 134 Issue 4, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+random+fields%22">Markov random fields</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Heterogeneous change detection (HeCD) is a highly valuable yet challenging task in remote sensing. However, existing HeCD methods primarily focus on well-registered images, without considering unregistered heterogeneous image pairs, which are more common in real-world applications. In this paper, we first analyze why unregistered images significantly complicate the HeCD problem: they not only cause boundary errors in change detection but also undermine the image transformation process required for making heterogeneous images comparable. These effects are further amplified by the intrinsic complexity of HeCD. In light of this, we propose an unsupervised iterative global mapping-local searching method (IGmLs) for HeCD subject to registration errors. Specifically, IGmLs utilizes the global mapping to transform images into a common structural space to enable the comparison of heterogeneous images, and uses the local searching to reduce the direct influence of registration errors on change metrics, which is based on the analysis that misregistration would cause an unwanted increment on the change metric in unchanged region. Then, IGmLs builds a Markov random field (MRF) model to combine the global mapping and local searching processes, which enhances the robustness to misregistrations by considering spatial correlations. Finally, an iterative framework is employed to backpropagate the matching and changing results to refine the global mapping and local searching processes, which further eliminates the indirect influence of changes and misregistration on the image transformation and change metrics. Extensive experiments on five datasets have demonstrated the effectiveness of the proposed IGmLs. The codes will be released at https://github.com/yulisun/IGmLs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Vision is the property of Springer Nature 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: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11263-025-02719-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Image registration Type: general – SubjectFull: Markov random fields Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Change-point problems Type: general Titles: – TitleFull: Iterative Global Mapping-Local Searching for Heterogeneous Change Detection with Unregistered Images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Yuli – PersonEntity: Name: NameFull: Wu, Junzheng – PersonEntity: Name: NameFull: Zhang, Han – PersonEntity: Name: NameFull: Li, Zhang – PersonEntity: Name: NameFull: Lei, Lin – PersonEntity: Name: NameFull: Kuang, Gangyao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 134 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Computer Vision Type: main |
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