论文标题
使用关键点对建筑物进行实例分割
Instance segmentation of buildings using keypoints
论文作者
论文摘要
在遥感图像解释的任务中，建筑细分非常重要。但是，现有的语义分割和实例分割方法通常会导致界限模糊的分割掩模。在本文中，我们提出了一个新颖的实例分割网络，用于在高分辨率遥感图像中构建分割。更具体地说，我们认为将单个建筑物分割为检测几个关键点。随后将检测到的关键点重新构成封闭的多边形，这是建筑物的语义边界。通过这样做，可以保留建筑物的锋利边界。实验是在选定的屋顶分割（AIRS）数据集的空中图像上进行的，与最先进的方法相比，我们的方法在定量和定性结果方面都能取得更好的性能。我们的网络是一种自下而上的实例分割方法，可以很好地保留几何细节。
Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.
