提出非配准变化检测新任务，挑战现有遥感方法鲁棒性
Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset
- 构建八类真实场景下的非配准问题模拟方案
- 通过图像变换将注册数据集转为非配准版本
- 验证顶尖方法在非配准下性能崩溃，适合遥感应急应用研究者
本文提出一种新型遥感变化检测任务——非配准变化检测，以应对自然灾害、人为事故及军事打击等突发事件的增多。针对当前对此类问题讨论不足，系统构建了八种可能引发非配准问题的真实场景。针对不同场景设计相应的图像变换策略，将现有注册变化检测数据集转化为非配准版本。实验表明，非配准情况会严重破坏当前先进方法的性能。代码与数据集已公开于 https://github.com/ShanZard/NRCD。
原文摘要 · Abstract (English)
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.
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