Shan Bowei, Fang Yong
School of Information Engineering, Chang'an University, Xi'an 710064, China.
Entropy (Basel). 2020 May 9;22(5):535. doi: 10.3390/e22050535.
This paper proposes a deep convolutional neural network model with encoder-decoder architecture to extract road network from satellite images. We employ ResNet-18 and Atrous Spatial Pyramid Pooling technique to trade off between the extraction precision and running time. A modified cross entropy loss function is proposed to train our deep model. A PointRend algorithm is used to recover a smooth, clear and sharp road boundary. The augmentated DeepGlobe dataset is used to train our deep model and the asynchronous training method is applied to accelerate the training process. Five salellite images covering Xiaomu village are taken as input to evaluate our model. The proposed E-Road model has fewer number of parameters and shorter training time. The experiments show E-Road outperforms other state-of-the-art deep models with 5.84% to 59.09% improvement, and can give the accurate predictions for the images with complex environment.
本文提出了一种具有编码器-解码器架构的深度卷积神经网络模型，用于从卫星图像中提取道路网络。我们采用ResNet-18和空洞空间金字塔池化技术来在提取精度和运行时间之间进行权衡。提出了一种改进的交叉熵损失函数来训练我们的深度模型。使用PointRend算法来恢复平滑、清晰和锐利的道路边界。增强后的DeepGlobe数据集用于训练我们的深度模型，并应用异步训练方法来加速训练过程。以覆盖小穆村的五幅卫星图像作为输入来评估我们的模型。所提出的E-Road模型具有较少的参数数量和较短的训练时间。实验表明，E-Road模型比其他现有最先进的深度模型性能更优，提升幅度为5.84%至59.09%，并且能够对具有复杂环境的图像给出准确的预测。
