基于残差神经网络的道路提取算法研究

针对遥感图像道路提取信息丢失问题,提出了一种基于残差神经网络的道路提取算法。首先构建编码器 解码器网络,结合预编码器以及空洞卷积模块进行训练,提取更多的语义信息;其次并联设计的空洞卷积模块加在编码器 解码器结构的中间部分,它可以对不同感受野的特征图进行特征提取;最后编码器 解码器之间采用跳连的方式进行多尺度的特征融合,学习更多低维和高维的特征。实验结果表明,在Massachusetts道路数据集上,该方法相比其他算法在Preci sion、Recall和F1 score性能指标上分别有11%、0.3%和7.4%的提升;同时在Accuracy指标上也达到了97.9%,相比于其他算法,该算法有一定的应用价值。

Research on Road Extraction Algorithm Based on Residual Neural Networks

In order to solve the problem of road extraction information loss in remote sensing images,a road extraction algorithm based on residual neural networks was proposed. Firstly,an encoder-decoder network was constructed,combined with pre-coder and dilated convolution module to extract more semantic information.Secondly,the parallel designed dilated convolution module was added to the middle part of the encoder-decoder structure,which could extract features of different receptive field features. Finally,the encoder-decoder used jumper to perform multi-scale feature fusion,learning more low-dimensional and high-dimensional features.In the Massachusetts road dataset,this method had 11 %,0.3 %,and 7.4 % improvement in Precision,Recall,and F1-score performance indicators. At the same time,it also achieved 97.9 % in the Accuracy index. Compared with other algorithms,the algorithm has certain application value.