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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]
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Database: Engineering Source
Description
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]
ISSN:09205691
DOI:10.1007/s11263-025-02719-6