论文标题
卫星图像中建筑物的准确多边形映射
Accurate Polygonal Mapping of Buildings in Satellite Imagery
论文作者
论文摘要
本文通过解决面具可逆性的问题来研究建筑物的多边形映射问题，该问题导致了基于学习的方法的预测面具和多边形之间的显着性能差距。我们通过利用层次监督（底部级顶点，中层线段和高级区域口罩）来解决这个问题，并提出了一种新型的相互作用机制，这些相互作用机制嵌入了来自不同级别的监督信号的特征嵌入，以获取可逆性建筑物的建筑物面具，以用于建筑物的多边形映射。结果，我们表明，学识渊博的可逆建筑面具占据了深度卷积神经网络的所有优点，用于建筑物的高绩效多边形映射。在实验中，我们评估了对Aicrowd和Inria的两个公共基准的方法。在Aicrowd数据集上，我们提出的方法对AP，APBOUNDARY和POLIS的指标获得了一致改进。对于Inria数据集，我们提出的方法还获得了IOU和准确性指标的竞争结果。型号和源代码可在https://github.com/sarahwxu上找到。
This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments and the high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on the two public benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU.
