Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on low-level details such as the edges. In this work, we propose a novel end-to-end edge-aware network, the EANet, and an edge-aware loss for getting accurate buildings from aerial images. Specifically, the architecture is composed of image segmentation networks and edge perception networks that, respectively, take charge of building prediction and edge investigation. The International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam segmentation benchmark and the Wuhan University (WHU) building benchmark were used to evaluate our approach, which, respectively, was found to achieve 90.19% and 93.33% intersection-over-union and top performance without using additional datasets, data augmentation, and post-processing. The EANet is effective in extracting buildings from aerial images, which shows that the quality of image segmentation can be improved by focusing on edge details.

中文翻译：

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EANet：边缘感知网络，用于从航空影像中提取建筑物

深度学习方法已用于从遥感图像中提取建筑物，并获得了最先进的性能。以前的大多数工作都强调了特征的多尺度融合或增强了更具接受性的场以实现全局特征，而不是专注于诸如边缘之类的低级细节。在这项工作中，我们提出了一种新颖的端到端边缘感知网络EANet，以及从航空影像中获取准确建筑物的边缘感知损失。具体而言，该架构由分别负责建筑物预测和边缘调查的图像分割网络和边缘感知网络组成。我们使用国际摄影测量与遥感学会（ISPRS）的波茨坦分割基准和武汉大学（WHU）的建筑基准来评估我们的方法，发现在不使用额外的数据集，数据扩充和后处理的情况下，分别实现了90.19％和93.33％的交接效率和最高性能。EANet可有效地从航拍图像中提取建筑物，这表明通过关注边缘细节可以提高图像分割的质量。