Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a valuable technique for enhancing model performance with minimal human interaction. For instance, the clicks-based interactive models have attracted much attention recently, however, their performance on large regions or complex areas still can be further improved, because they cannot provide accurate semantics or shape prior information of the change regions for the interactive models, as we know that the shape and semantic features of changed regions in remote sensing imagery are typically irregular and complex. Scribble-based interactive form, which can accurately represent the shape or semantic features of the changed regions, thus it is quite suitable for change detection tasks in remote sensing imagery. Therefore, we proposed a novel interactive deep learning model called ScribbleCDNet in this manuscript, which pioneered the use of scribble as an interactive form for detecting change in bi-temporal high-resolution remote sensing imageries. Compared with the widely used clicks-based interactive deep learning models, the proposed ScribbleCDNet acquired superior results on four open-sourced change detection datasets. Last but not least, we also developed an interactive change detection tool with a user-friendly graphical interface, and it can aid researchers in conducting change detection or generating training samples conveniently. Moreover, the proposed ScribbleCDNet can also inspire researchers to develop other interactive deep-learning models related to semantic segmentation, landcover classification, or object extraction in high-resolution remote sensing imageries.

中文翻译：

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ScribbleCDNet：通过涂鸦交互对高分辨率遥感图像进行变化检测

近年来，使用端到端深度学习方法对高分辨率遥感图像进行变化检测引起了广泛关注。尽管如此，端到端模型在复杂场景上的性能仍然有限。交互式深度学习模型已被证明是一种以最少的人类交互来增强模型性能的有价值的技术。例如，基于点击的交互模型最近引起了广泛关注，但是它们在大区域或复杂区域上的性能仍然可以进一步提高，因为它们无法为交互模型提供准确的语义或形状变化区域的先验信息，众所周知，遥感影像中变化区域的形状和语义特征通常是不规则且复杂的。基于涂鸦的交互形式，可以准确地表示变化区域的形状或语义特征，因此非常适合遥感影像中的变化检测任务。因此，我们在本手稿中提出了一种名为 ScribbleCDNet 的新型交互式深度学习模型，该模型开创性地使用涂鸦作为一种交互形式来检测双时态高分辨率遥感图像的变化。与广泛使用的基于点击的交互式深度学习模型相比，所提出的 ScribbleCDNet 在四个开源变化检测数据集上获得了优异的结果。最后但并非最不重要的一点是，我们还开发了一个交互式变化检测工具，具有用户友好的图形界面，它可以帮助研究人员方便地进行变化检测或生成训练样本。此外，所提出的 ScribbleCDNet 还可以激励研究人员开发与高分辨率遥感图像中的语义分割、土地覆盖分类或对象提取相关的其他交互式深度学习模型。