In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (CNN) as the backbone to predict energy maps, which are further utilized to construct an energy function. A polygon-based contour is then evolved via minimizing the energy function, of which the minimum defines the final segmentation. Instead of parameterizing the contour using Euclidean coordinates, we adopt polar coordinates, i.e., rays, which not only prevents self-intersection but also simplifies the design of the energy function. Moreover, we propose a loss function that directly encourages the contours to match building boundaries. Our DARNet is trained end-to-end by back-propagating through the energy minimization and the backbone CNN, which makes the CNN adapt to the dynamics of the contour evolution. Experiments on three building instance segmentation datasets demonstrate our DARNet achieves either state-of-the-art or comparable performances to other competitors.

本文提出了一种名为DARNet的深度学习网络来实现自动化建筑物分割，通过使用卷积神经网络对能量映射进行预测，并通过对能量函数进行最小化，采用基于极坐标的轮廓演变来获得最终的建筑物分割，同时使用一种新的损失函数直接鼓励边界匹配，实验证明该网络在三个建筑实例分割数据集上取得了媲美或领先竞争对手的表现

DARNet: 深度主动射线网络用于建筑物分割