Building extraction in VHR RSIs remains to be a challenging task due to occlusion and boundary ambiguity problems. Although conventional convolutional neural networks (CNNs) based methods are capable of exploiting local texture and context information, they fail to capture the shape patterns of buildings, which is a necessary constraint in the human recognition. In this context, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns, thus improving the accuracy of building segmentation. In the proposed ASLNet, we introduce the adversarial learning strategy to explicitly model the shape constraints, as well as a CNN shape regularizer to strengthen the embedding of shape features. To assess the geometric accuracy of building segmentation results, we further introduced several object-based assessment metrics. Experiments on two open benchmark datasets show that the proposed ASLNet improves both the pixel-based accuracy and the object-based measurements by a large margin. The code is available at: https://github.com/ggsDing/ASLNet

翻译：由于封闭性和边界模糊性问题,VHRRSI的建筑采掘仍然是一项具有挑战性的任务。虽然传统的进化神经网络(CNNs)基于传统神经网络(CNNs)能够利用当地的质地和背景信息,但它们未能捕捉建筑物的形状模式,这是人类认知的一个必要制约。在这方面,我们提议建立一个对抗形状学习网络(ASLNet)来模拟建筑形状模式,从而提高建筑分割的准确性。在拟议的ASLNet中,我们引入对抗式学习战略,以明确模拟形状限制,以及CNN的形状调节器来加强形状特征的嵌入。为了评估建筑分解结果的几何精确度,我们进一步引入了几个基于目标的评估指标。在两个开放式基准数据集上进行的实验表明,拟议的ASLNet既改进了像素基精确度,又大大改进了基于对象的测量。该代码可在以下网址查阅:https://github.com/ggsDing/ASLNet。