Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks
Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks
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DOI：
10.3390/s19194115
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发表时间：
2019-10-01
期刊：
影响因子：
3.9
通讯作者：
Tong, Ling
中科院分区：
文献类型：
--
作者：
Li, Yuxia;Peng, Bo;Tong, Ling
道路是基础设施的重要组成部分，道路的提取已成为遥感领域的一个重要课题。由于深度学习已经成为图像处理和信息提取的主流方法，因此利用神经网络进行道路提取的研究受到了越来越多的关注。提出了一种改进的神经网络方法，用于无人机遥感图像中道路的提取。D-Linknet最初被认为具有高性能;然而，网络的巨大规模降低了计算效率。针对目前流行的D-LinkNet计算效率低的问题，本文做了以下改进：（1）用词干块代替初始块。(2)使用新结构基于ResNet单元重建整个网络，从而构建改进的神经网络D-Linknetplus。(3)在DBlock之前添加1 x 1卷积层，以减少输入特征图，减少参数并提高计算效率。在DBlock之后添加另一个1 x 1卷积层，以恢复所需数量的输出通道。在此基础上，构建了另一种改进的神经网络B-D-LinknetPlus。对神经网络进行了比较，并利用马萨诸塞州道路数据集进行了验证。结果表明，改进的神经网络有助于减少网络规模，提高道路提取所需的精度。
Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 x 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 x 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.