利用拓扑进行卫星和航空影像中的域自适应道路分割
Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery
AI总结：
本文提出拓扑感知的无监督域自适应道路分割方法，通过道路骨架辅助约束、跨头一致性损失和基于连通性的伪标签细化提升跨域连续性，在 SpaceNet 到 DeepGlobe 上显著优于现有方法。
摘要：
通过从遥感影像中分割道路来获取道路的精确信息，对于自动驾驶汽车、城市发展与规划以及实现可持续发展目标等许多现实应用都非常有用。道路仅占图像的一小部分，并且其外观、类型、宽度、海拔、方向等在不同地理区域之间表现出巨大差异。此外，由于城市化风格、规划和自然环境的差异，道路沿线区域也存在显著不同。由于训练域和测试域之间存在这些变化，道路分割算法无法泛化到新的地理位置。与一般的域对齐场景不同，道路分割没有场景结构，通用的域自适应方法无法强制保证连续性、连通性、平滑性等拓扑性质，从而导致域对齐效果下降。在本文中，我们提出了一种用于遥感影像道路分割的拓扑感知无监督域自适应方法。具体而言，我们预测道路骨架，将其作为施加拓扑约束的辅助任务。为了强制道路预测与骨架预测保持一致，尤其是在无标注目标域中，我们在骨架预测头和道路分割头之间定义了一致性损失。此外，对于自训练，我们在道路分割头和骨架分割头上使用基于连通性的伪标签细化策略过滤掉含噪伪标签，从而避免孔洞和不连续。在基准数据集上进行的大量实验表明，与现有最先进方法相比，所提方法具有有效性。具体而言，在从 SpaceNet 到 DeepGlobe 的自适应任务中，所提方法在 IoU、F1-score 和 APLS 上分别以至少 6.6%、6.7% 和 9.8% 的优势优于对比方法。
英文摘要：
Getting precise aspects of road through segmentation from remote sensing imagery is useful for many real-world applications such as autonomous vehicles, urban development and planning, and achieving sustainable development goals. Roads are only a small part of the image, and their appearance, type, width, elevation, directions, etc. exhibit large variations across geographical areas. Furthermore, due to differences in urbanization styles, planning, and the natural environments; regions along the roads vary significantly. Due to these variations among the train and test domains, the road segmentation algorithms fail to generalize to new geographical locations. Unlike the generic domain alignment scenarios, road segmentation has no scene structure, and generic domain adaptation methods are unable to enforce topological properties like continuity, connectivity, smoothness, etc., thus resulting in degraded domain alignment. In this work, we propose a topology-aware unsupervised domain adaptation approach for road segmentation in remote sensing imagery. Specifically, we predict road skeleton, an auxiliary task to impose the topological constraints. To enforce consistent predictions of road and skeleton, especially in the unlabeled target domain, the conformity loss is defined across the skeleton prediction head and the road-segmentation head. Furthermore, for self-training, we filter out the noisy pseudo-labels by using a connectivity-based pseudo-labels refinement strategy, on both road and skeleton segmentation heads, thus avoiding holes and discontinuities. Extensive experiments on the benchmark datasets show the effectiveness of the proposed approach compared to existing state-of-the-art methods. Specifically, for SpaceNet to DeepGlobe adaptation, the proposed approach outperforms the competing methods by a minimum margin of 6.6%, 6.7%, and 9.8% in IoU, F1-score, and APLS, respectively.
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