论文标题
使用对抗和正规化损失的卫星图像中建筑边界的正规化
Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses
论文作者
论文摘要
在本文中，我们提出了一种在卫星图像中使用完全卷积的神经网络在卫星图像中建立边界改进和正则化的方法，该网络训练了对抗性和正则损失的组合。与纯掩码R-CNN模型相比，总体算法可以在准确性和完整性方面达到同等的性能。但是，与产生不规则足迹的面膜R-CNN不同，我们的框架产生了正式和视觉上令人愉悦的建筑边界，这些界限在许多应用中都是有益的。
In this paper we present a method for building boundary refinement and regularization in satellite images using a fully convolutional neural network trained with a combination of adversarial and regularized losses. Compared to a pure Mask R-CNN model, the overall algorithm can achieve equivalent performance in terms of accuracy and completeness. However, unlike Mask R-CNN that produces irregular footprints, our framework generates regularized and visually pleasing building boundaries which are beneficial in many applications.
