论文标题
基于涂鸦的弱监督的深度学习，从遥感图像中提取道路表面
Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images
论文作者
论文摘要
使用深度学习方法从遥感图像中提取的道路表面提取已经达到了良好的性能，而大多数现有方法基于完全监督的学习，这需要大量的培训数据，并用辛苦的每像素注释。在本文中，我们提出了一种名为ScroadeXtractor的基于涂鸦的弱监督的道路表面提取方法，该方法从易于访问的涂鸦（例如中心线）中学习，而不是密集注释的道路表面地面真实。为了传播从稀疏涂鸦到未标记像素的语义信息，我们引入了道路标签传播算法，该算法既考虑道路网络的基于缓冲区的属性以及超级像素的颜色和空间信息。我们设计的双支台面编码器网络用于训练由道路标签传播算法生成的提案掩模，该掩模由我们设计的双支台面编码器网络，该网络由语义分割分支和一个辅助边界检测分支组成。我们在三个不同的道路数据集上进行实验，这些数据集由全球高分辨率遥感卫星和空中图像组成。结果表明，scroadextractor超过了经典的涂鸦分割方法，即在联合（IOU）指标上的交叉点，超过20％，并且表现优于最先进的基于涂鸦的弱监督方法至少4％。
Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this paper, we propose a scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground-truths. To propagate semantic information from sparse scribbles to unlabeled pixels, we introduce a road label propagation algorithm which considers both the buffer-based properties of road networks and the color and spatial information of super-pixels. The proposal masks generated from the road label propagation algorithm are utilized to train a dual-branch encoder-decoder network we designed, which consists of a semantic segmentation branch and an auxiliary boundary detection branch. We perform experiments on three diverse road datasets that are comprised of highresolution remote sensing satellite and aerial images across the world. The results demonstrate that ScRoadExtractor exceed the classic scribble-supervised segmentation method by 20% for the intersection over union (IoU) indicator and outperform the state-of-the-art scribble-based weakly supervised methods at least 4%.
