Recent advancements have significantly improved the field of segmentation-based change detection, particularly in the context of remote-sensing images. However, change detection datasets generally lack segmentation annotations, and the required labeling process is resource-intensive. We propose an improved change detection method based on segmentation to address this challenge. First, change detection annotations are converted to incomplete segmentation annotations through label matching. During segmentation, we utilize the focused information-guided segmentation method (FIGS) and a greenness index to provide prior information during training, guiding the model using accurately labeled regions. Finally, we generate a change map using pretrained features obtained from the segmentation stage. We demonstrate the robustness of our proposed label-matching process by comparing the results to a correctly matched dataset and show that incorporating FIGS and the greenness index improves the segmentation performance. Our method achieves effective change detection results even in scenarios associated with a shortage of annotations.

中文翻译：

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基于有限标注分割的变化检测聚焦信息学习方法

最近的进展显着改善了基于分割的变化检测领域，特别是在遥感图像的背景下。然而，变化检测数据集通常缺乏分段注释，并且所需的标记过程是资源密集型的。我们提出了一种基于分割的改进的变化检测方法来应对这一挑战。首先，通过标签匹配将变化检测注释转换为不完整的分割注释。在分割过程中，我们利用聚焦信息引导分割方法（FIGS）和绿色指数在训练过程中提供先验信息，指导模型使用准确标记的区域。最后，我们使用从分割阶段获得的预训练特征生成变化图。我们通过将结果与正确匹配的数据集进行比较来证明我们提出的标签匹配过程的鲁棒性，并表明合并无花果和绿色指数可以提高分割性能。即使在注释短缺的情况下，我们的方法也能实现有效的变化检测结果。