Mainstreamed weakly supervised road extractors rely on highly confident pseudo-labels propagated from scribbles, and their performance often degrades gradually as the image scenes tend various. We argue that such degradation is due to the poor model's invariance to scenes with different complexities, whereas existing solutions to this problem are commonly based on crafted priors that cannot be derived from scribbles. To eliminate the reliance on such priors, we propose a novel Structure-aware Mixup and Invariance Learning framework (SA-MixNet) for weakly supervised road extraction that improves the model invariance in a data-driven manner. Specifically, we design a structure-aware Mixup scheme to paste road regions from one image onto another for creating an image scene with increased complexity while preserving the road's structural integrity. Then an invariance regularization is imposed on the predictions of constructed and origin images to minimize their conflicts, which thus forces the model to behave consistently on various scenes. Moreover, a discriminator-based regularization is designed for enhancing the connectivity meanwhile preserving the structure of roads. Combining these designs, our framework demonstrates superior performance on the DeepGlobe, Wuhan, and Massachusetts datasets outperforming the state-of-the-art techniques by 1.47%, 2.12%, 4.09% respectively in IoU metrics, and showing its potential of plug-and-play. The code will be made publicly available.

一种新颖的结构感知混合和不变性学习框架 (SA-MixNet) 被提出用于弱监督道路提取，该框架通过数据驱动的方式提高了模型的不变性，结合结构感知混合策略、不变性正则化及基于鉴别器的正则化方法，使得该框架在 DeepGlobe、Wuhan 和 Massachusetts 数据集中的 IoU 指标分别优于现有技术 1.47%、2.12% 和 4.09%，表现出其即插即用的潜力，并将源代码公开发布。

SA-MixNet：结构感知混合和不变性学习用于遥感图像中基于草图监督的道路提取