论文标题
DSM改进，具有深层编码器网络
DSM Refinement with Deep Encoder-Decoder Networks
论文作者
论文摘要
3D城市模型可以从航空图像中产生。但是，计算出的DSM遭受了必须在耗时的过程中手动清理的噪声，人工制品和数据孔。这项工作提出了一种自动完善此类DSM的方法。关键思想是从参考数据传授一个城市区域的特征。为了实现此目标，提出了由L1规范和功能损失组成的损失函数。这些功能是使用预先训练的图像分类网络构建的。为了学习更新高度图，网络体系结构的设置是根据深度残留学习和编码器造型结构的概念来设置的。结果表明，这种组合非常有效地保留相关的几何结构，同时消除了不希望的人工制品和噪声。
3D city models can be generated from aerial images. However, the calculated DSMs suffer from noise, artefacts, and data holes that have to be manually cleaned up in a time-consuming process. This work presents an approach that automatically refines such DSMs. The key idea is to teach a neural network the characteristics of urban area from reference data. In order to achieve this goal, a loss function consisting of an L1 norm and a feature loss is proposed. These features are constructed using a pre-trained image classification network. To learn to update the height maps, the network architecture is set up based on the concept of deep residual learning and an encoder-decoder structure. The results show that this combination is highly effective in preserving the relevant geometric structures while removing the undesired artefacts and noise.
