Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913838700429312 |
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| author | Shan, Zhe Zhou, Lei Mao, Liu Chen, Shaofan Ren, Chuanqiu Xie, Xia |
| author_facet | Shan, Zhe Zhou, Lei Mao, Liu Chen, Shaofan Ren, Chuanqiu Xie, Xia |
| contents | In this study, we propose a novel remote sensing change detection task, non-registration change detection, to address the increasing number of emergencies such as natural disasters, anthropogenic accidents, and military strikes. First, in light of the limited discourse on the issue of non-registration change detection, we systematically propose eight scenarios that could arise in the real world and potentially contribute to the occurrence of non-registration problems. Second, we develop distinct image transformation schemes tailored to various scenarios to convert the available registration change detection dataset into a non-registration version. Finally, we demonstrate that non-registration change detection can cause catastrophic damage to the state-of-the-art methods. Our code and dataset are available at https://github.com/ShanZard/NRCD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset Shan, Zhe Zhou, Lei Mao, Liu Chen, Shaofan Ren, Chuanqiu Xie, Xia Computer Vision and Pattern Recognition Image and Video Processing In this study, we propose a novel remote sensing change detection task, non-registration change detection, to address the increasing number of emergencies such as natural disasters, anthropogenic accidents, and military strikes. First, in light of the limited discourse on the issue of non-registration change detection, we systematically propose eight scenarios that could arise in the real world and potentially contribute to the occurrence of non-registration problems. Second, we develop distinct image transformation schemes tailored to various scenarios to convert the available registration change detection dataset into a non-registration version. Finally, we demonstrate that non-registration change detection can cause catastrophic damage to the state-of-the-art methods. Our code and dataset are available at https://github.com/ShanZard/NRCD. |
| title | Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2505.09939 |