Road extraction from remote sensing images plays an important role in traffic management, urban planning, automatic vehicle navigation and emergency management. It is a hot issue that how to extract effectively road information from remote sensing images. Here, a new model, namely parallel softplus network (PSNet), has been proposed, which uses parallel network structure and softplus activation function. Specially, the model uses a new weight initialization for extraction effectiveness. Moreover, compared with the popular models, it extracts more complete and continuous road information on the same road remote sensing images. Meanwhile, it outperforms other extraction models, with a high F1-score . Experimental results indicate that it is a promising model, which effectively extracts road information from remote sensing images with a little noise.

中文翻译：

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使用并行 Softplus 网络从遥感图像中提取道路

从遥感图像中提取道路在交通管理、城市规划、车辆自动导航和应急管理中发挥着重要作用。如何有效地从遥感影像中提取道路信息是一个热点问题。这里提出了一种新的模型，即并行 softplus 网络（PSNet），它使用并行网络结构和 softplus 激活函数。特别地，该模型使用新的权重初始化来提高提取效率。而且，与流行的模型相比，它在同一道路遥感图像上提取了更完整和连续的道路信息。同时，它优于其他提取模型，具有较高的 F1 分数。实验结果表明，它是一种很有前景的模型，可以有效地从遥感图像中提取道路信息，噪声很小。